Edge AI in Nature: Insect-Inspired Neuromorphic Reflex Islands for Safety-Critical Edge Systems

Abstract

Insects achieve millisecond sensor–motor loops with tiny sensors, compact neural circuits, and powerful actuators, embodying the principles of Edge AI. We present a comprehensive architectural blueprint translating insect neurobiology into a hardware–software stack: a latency-first control hierarchy that partitions tasks between a fast, dedicated Reflex Tier and a slower, robust Policy Tier, with explicit WCET envelopes and freedom-from-interference boundaries. This architecture is realized through a neuromorphic Reflex Island utilizing spintronic primitives, specifically MRAM synapses (for non-volatile, innate memory) and spin-torque nano-oscillator (STNO) reservoirs (for temporal processing), to enable instant-on, memory-centric reflexes. Furthermore, we formalize the biological governance mechanisms, demonstrating that, unlike conventional ICEs and miniturbines that exhibit narrow best-efficiency islands, insects utilize active thermoregulation and DGC (Discontinuous Gas Exchange) to maintain nearly constant energy efficiency across a broad operational load by actively managing their thermal set-point, which we map into thermal-debt and burst-budget controllers. We instantiate this integrated bio-inspired model in an insect-like IFEVS thruster, a solar cargo e-bike with a neuromorphic safety shell, and other safety-critical edge systems, providing concrete efficiency comparisons, latency, energy budgets, and safety-case hooks that support certification and adoption across autonomous domains.

1. Introduction: Insects as Canonical Edge-AI Systems

Insects execute navigation, stabilization, landing, foraging, and escape entirely on-device, using parallel sensors (compound eyes, ocelli, halteres, antennae) connected by short neural pathways to actuators with ms-range latency [1,2,3,4]. They fuse wide-field optic flow with inertial/gyroscopic signals for phase-locked wing control [1,2,3], mapping well to latency-first embedded loops in robotics and vehicles. Their neural organization cleanly separates fast stabilization from slower planning/learning, matching the Reflex/Policy split we adopt [5,6,7,8,9].

Recent surveys emphasize that Edge AI should be designed in a task-oriented way, jointly balancing inference quality, latency, energy, and reliability and, at the network edge, co-designing integrated sensing, communication, and computation [10,11]. In this paper, we focus on a complementary niche: safety-critical, on-device closed-loop control, where certifiable timing bounds and safety invariants dominate the architecture and where “Policy” intelligence is deliberately bounded by a hard real-time Reflex safety shell.

Applicability (and limits). The insect template is most useful when (i) the plant must be stabilized under hard deadlines, (ii) failures have immediate safety consequences, and (iii) energy/thermal budgets force event-driven sensing and local decision-making. In contrast, tasks that are purely offline, cloud-assisted, or dominated by high-throughput batch inference need not adopt a Reflex/Policy split.

1.1. The Fly (Stabilization Specialist)

Flies combine halteres (mechanical gyroscopes) with wide-field vision to stabilize attitude within tens of milliseconds [1,2,3] Figure 1. A “giant-fiber” pathway triggers evasive maneuvers with minimal synapses, illustrating an architectural bias for short, deterministic, high-priority reflexes [4,5] (

Table 1. Key components of the fly flight-stabilization sensorimotor system (sensors, computation, and control loops) and their analogs in edge-AI architectures.

Component	Description and Function	Edge-AI Analogy
Sensors (Fast & Complementary)	“Compound eyes (wide FOV, very high temporal resolution); Ocelli (simple light sensors for attitude); Halteres (gyroscopes sensing body rotation via Coriolis forces); Antennae/hairs (airflow and vibration).”	IMU/Gyroscopes + Global-shutter/Event Cameras + Airflow Sensors.
On-board Computation	“Optic lobe motion detectors (parallel ‘flow’ filters); Reflex pathways (e.g., ‘giant fiber’ escape route) prioritizing latency; Central complex (orientation); Mushroom bodies (learning).”	Fixed-function accelerators (for optic flow); Low-latency neuromorphic circuits (Reflex Tier).
Control Loops	Inner Loop (Stabilization): halteres + ocelli + wide-field motion allows wing adjustments in a few–tens of ms. Outer Loop (Goal): visual flow + odor/airflow allows course/target selection over longer windows.	Two-tier control: Reflex Tier (µs-ms) for stabilization; Policy Tier (ms-s) for planning.

).

1.2. The Bee (Navigation and Task Specialist)

Bees add robust heuristics for speed control and landing, e.g., holding optic-flow expansion roughly constant as well as a celestial compass (polarization sensitivity) for long-range navigation [6,7,8,9] (

Table 2. Key components of the bee navigation and task-specialist sensorimotor system (sensors, on-board computation, and control loops) and their analogs in edge-AI architectures.

Component	Description and Function	Edge-AI Analogy
Sensors	Compound eyes (incl. UV) + polarization sensitivity (sun compass); Ocelli (attitude); Antennae (olfaction + mechanosensation); Body hairs (micro-climate).	Polarization/UV Sensors; Odorimeters; Temperature/Contact Sensors.
On-board Computation	Optic flow odometry (distance estimation); Central complex (heading/compass and path integration); Mushroom bodies (associative learning); Task switching with minimal memory.	Visual Odometry (2D V-SLAM); Rule-based Policies (Heuristics); Local Learning (small neural networks).
Control Loops	Landing/Altitude: Maintain constant optic-flow expansion allows smooth landings without absolute altitude. Navigation: Polarization compass + optic-flow odometer + odor cues allows waypoint-style guidance.	“Non-linear control based on heuristics (e.g., adaptive Proportional-Integral-Derivative); Low-cost sensor fusion.”

). In the central complex, ring-attractor circuits maintain a persistent heading “bump,” providing a compact Policy signal to bias reflexes [8,9].

Multi-modal sensing in bees—wide-field vision, polarized-sky compass, odor, and wind cues, feeds into fast navigation Reflex pathways and slower Policy/Task pathways Figure 2. This Reflex–Policy partition provides the biological template for our Edge-AI Reflex Islands and Policy layer Figure 2.

During landing, bees rely on optic flow: the visual angle α(t) to the touchdown point expands as they approach, and a simple heuristic keeps the angular expansion rate dα/dt approximately constant, yielding exponential deceleration and a soft landing [6] Figure 3. An optic-flow sensor supplies α and dα/dt to a non-linear landing controller that modulates thrust to hold dα/dt near constant Figure 3.

Bee long-range navigation can be implemented as a robust, low-bandwidth sensor-fusion pipeline that combines polarized-sky cues (sun compass) with heading estimation Figure 4 [7]. In the central complex, a ring-attractor “head-direction” circuit maintains a persistent heading bump that biases downstream reflexes and stabilizes steering decisions Figure 5 [8,9].

2. Latency-First Architecture (Biology → Engineering)

Insect biology provides a model for a two-tier control architecture, essential for safety-critical Edge-AI systems: it prioritizes the speed and reliability of local reflexes over the complexity of global planning. This latency-first approach ensures that critical stabilization and emergency responses occur via the shortest, most deterministic pathway possible.

2.1. Two-Tier Control

We implement two tiers: a Reflex Tier (near sensors/actuators; µs–ms) and a Policy Tier (ms–s), communicating via timestamped, lock-free queues. Reflex handles stabilization and safety; Policy handles mapping, heuristics, and goals. This mirrors insect hierarchies and makes timing budgets analyzable for safety certification [4,5,12,13]

Table 3. Two-tier biological-to-engineering mapping between insect-inspired Reflex and Policy control layers, with typical latencies and functional roles.

Biological Tier	Engineering Tier	Typical Latency	Function
Reflex	Reflex Tier (Neuromorphic Island)	Microseconds–Milliseconds (µs–ms)	“Stabilization, immediate reactions, low-level control (e.g., halteres control the steering of muscles).”
Policy	Policy Tier (RT Core/NPU)	Tens–Hundreds of Milliseconds (ms–s)	“Navigation, path planning, associative learning, goal selection (e.g., central complex → reflexes).”

. Design principles are: co-locate sensors/compute/drivers; wire interrupt → DMA → neuromorphic Reflex → RT core → PWM/FOC; hold end-to-end stabilization <5 ms with critical safeties <1 ms; and export only goal states upstream.

Table 4. Example Reflex-loop WCET breakdown (optic flow + IMU → steering PWM).

Stage	Function (Example Stack)	WCET (µs)	Cumulative (µs)
1	DVS/IMU exposure leads to interrupt assertion	50	50
2	DMA + time-stamp + spike encoding into Reflex Island	150	200
3	FF-SNN layer 1 (elementary motion detectors)	800	1000
4	STNO reservoir / RSNN update + readout	1200	2200
5	Reflex decision logic + watchdog comparators	400	2600
6	RT core arbitration, FOC current reference update	400	3000
7	PWM/timer update + gate-driver propagation	400	3400

Example stack: Infineon AURIX TC39x (300 MHz, 40 nm), Everspin STT-MRAM (35 ns access), Loihi 2-class SNN core (14 nm, ~2 µJ/inference). Times are worst-case at 125 °C, 85% V_min.

gives an illustrative WCET envelope for a reference Reflex loop inspired by “haltere + optic flow → steering muscles”, instantiated on a representative automotive stack (AURIX-class MCU + MRAM device + Loihi-class SNN). Times are worst-case at the hot electrical corner (high temperature, low voltage), including guard bands.

The resulting end-to-end Reflex WCET is 3.4 ms against a 5 ms design budget, leaving ≈32% margin. Critical hardwired safety paths (e.g., limit-switch or E-stop → power stage) bypass the neuromorphic layers entirely and are kept below 1 ms. The table is not meant as a new benchmark, but as a concrete example of how the latency-first architecture supports WCET reasoning at the component and loop level.

2.2. Symmetry, Invariants, and a Control-Theoretic Interpretation

The symmetry claims in Section 1 can be stated in standard dynamical system terms. Rotational and bilateral symmetry reduce the effective state space (many perturbations map to equivalent corrective actions), while temporal symmetry corresponds to stable limit-cycle behavior (CPGs/wingbeat phase), where control acts primarily by phase and small amplitude corrections rather than full-state recomputation. In engineering terms, this motivates phase-based controllers, equivariant feature extraction, and periodic state estimators that are naturally robust to disturbances and sensor dropouts.

For safety-critical systems, “invariants” can be made explicit as forward-invariant safe sets in the state space. A practical realization is a safety filter (Reflex Tier) that projects any nominal Policy command into a set of admissible inputs that preserve invariance; control barrier functions (CBFs) provide a widely used formalism to encode such invariants and enforce them via real-time quadratic programs or lightweight approximations suitable for embedded deployment [14]. This gives a mathematical footing to the role of the Reflex Tier: not only fast, but also a certified guardian of invariants, with the Policy Tier treated as a performance-oriented module.

Task-oriented design. Latency is a necessary metric for closed-loop safety, but it is not sufficient. The proposed framework can be viewed as a constrained multi-objective design: maximize task utility (e.g., stabilization quality or perception accuracy) subject to hard constraints on Reflex WCET, energy per decision, thermal debt, and bounded fault impact (FFI). In practice, the Reflex Tier enforces constraints (timing + invariants), while the Policy Tier explores the quality/energy frontier under those constraints—matching the task-oriented perspective in recent Edge-AI system surveys [10,11].

3. Neuromorphic and Spintronic Hardware

The physical implementation of insect Edge-AI principles finds a natural analogy in neuromorphic and spintronic technologies.

3.1. Why Neuromorphic Matches the Insect Edge

Spiking Neural Networks (SNNs) are the central concept of neuromorphic computing and directly mirror insect neural codes: computation occurs only on significant changes in input (events), not on continuous data streams. This event-driven nature drastically reduces data movement and power consumption while maintaining the µs–ms responsiveness required for reflexes. SNNs naturally handle temporal coding and oscillations, which map directly to biological principles like Central Pattern Generators (CPGs) and the rhythmic phase control necessary for insect flight timing. Furthermore, the inherent architecture of neuromorphic chips, with memory co-located or interwoven with processing elements, supports local, on-device adaptation (e.g., STDP-like plasticity), similar to the associative learning observed in the insect mushroom bodies, bypassing the need to send large datasets to the cloud for learning updates. Spiking computation is event-driven and temporal, reducing data movement and power while meeting µs–ms latency; it naturally supports oscillations/CPGs and online adaptation [15,16,17]. Recent training and design approaches for deep SNNs are reviewed in [18].

To realize the Reflex Island, the SNN architecture should be a hybrid model: an initial Feed-Forward SNN (FF-SNN) layer executes rapid event filtering and early feature extraction (e.g., elementary motion detectors for optic flow), which then feeds into the core Recurrent SNN (RSNN)/Spintronic Reservoir layer responsible for state-space integration (e.g., haltere-visual fusion). Benchmarks for this layer must target ultra-low latency (end-to-end tau < 5 ms with extreme energy efficiency (aiming for power consumption of <1 pJ or total power < 1 mW for the dedicated reflex loop), aligning the system with the insect’s goal of continuous, deterministic stabilization.

Spiking neuromorphic hardware has already been validated at scale on several platforms: Intel’s Loihi chips for on-chip learning, the SpiNNaker many-core machine, IBM’s TrueNorth neurosynaptic processor, and BrainChip’s Akida edge SoC, all of which demonstrate low-power, event-driven inference in real applications [16,19,20,21].

Beyond chip demonstrations, the relevant validation target is closed-loop perception–action. Event-based vision offers a mature sensor modality for Reflex Islands (low latency, high dynamic range), with extensive peer-reviewed surveys and benchmarks for optic flow, tracking, and control-relevant features [22]. Closed-loop benchmarking for neuromorphic systems is itself an active research direction [23]. Robotics and autonomy prototypes increasingly pair event cameras with SNN or hybrid neuromorphic pipelines to achieve µs–ms reaction times at low power, providing concrete experimental anchors for the architecture proposed here.

3.2. Spintronic Primitives as “Physical Synapses and Neurons”

Spintronic devices offer the ideal physical building blocks for constructing the ultra-low-power, instant-on “Reflex Island”. Their fundamental advantage is that they are “memory-centric,” enabling computation to happen at the site of memory. This approach directly attacks the primary bottleneck in conventional computing, the energy and latency cost of moving data between a separate processor and memory, which is precisely the problem insects solve with their short, efficient neural pathways.

This spintronic-neuromorphic architecture is built from two key components:

• MRAM synapses (MTJs). Non-volatile, instant-on “innate” reflex weights, perfect for a cold-start Reflex Island [12,24]. The “synapses,” which store the network’s knowledge, are implemented using Magnetic RAM (MRAM). These are physical analogs for synapses, built from Magnetic Tunnel Junctions (MTJs). Their most critical property is non-volatility: they store the synaptic weights (the “innate reflexes”) permanently, without requiring any power. This provides two transformative advantages for an edge device.
• STNO neurons/reservoirs. GHz nano-oscillators that process temporal streams (IMU, event cameras) as rich reservoirs, mapping to optic-flow and reflex transforms [12,24,25].

Together they yield a memory-centric, event-driven, instant-on reflex substrate aligned with insect physiology [12,13,15,16,17,24,25]. The “neurons,” which process information, are implemented using Spin-Torque Nano-Oscillators (STNOs). These are compact, high-frequency (GHz) oscillators that naturally function as spiking neurons. Their unique strength is processing temporal information. STNOs are ideal for this, especially when configured as Reservoir Computers. An “STNO reservoir” is a complex, dynamic system of oscillators that can take high-speed temporal data streams (like from an event camera or IMU) and perform powerful sensorimotor transformations, making them a perfect fit for complex tasks like optic flow analysis and stabilization control.

The resulting MRAM–STNO stack is not speculative: it is explicitly tracked in industrial roadmaps such as the IRDS ‘Emerging Research Devices/Beyond CMOS’ chapter, the 2022 Roadmap on Neuromorphic Computing and Engineering, and the SRC/SIA Decadal Plan for Semiconductors, all of which identify spintronic memories and oscillators as leading candidates for ultra-low-power, neuromorphic compute fabrics [12,26,27,28].

While the MRAM synapse is mature (TRL 8–9) and already commercialized for non-volatile memory, the STNO-based reservoir neuron remains at mid-development (TRL 4–5) due to integration challenges; consequently, the full Reflex Island chip incorporating both primitives for deterministic, event-driven control is realistically projected to achieve integrated laboratory prototypes (TRL 5–6) within 2–3 years, leading to initial deployment in specialized, latency-critical domains like micro-robotics or medical sensing (TRL 7+) within the next 5 years Figure 6.

Commercial MRAM examples are: Everspin EMD3D256M16 (256 Mb, 35 ns, TRL 9) [https://www.everspin.com/supportdocs/EMD3D256M16G2-150CBS1, accessed on 20 December 2025]; TSMC 22ULL eMRAM (16 Mb embedded, TRL 8) [https://www.techinsights.com/blog/memory/disruptive-technology-tsmc-22ull-emram, accessed on 20 December 2025]; Avalanche MRAM-based accelerators (TRL 7–8). Per IRDS 2022, embedded STT-MRAM reached volume production in 2020–2022 at leading fabs Figure 6.

• Sensing: Event streams from sensors (e.g., event cameras, IMU) are converted into spikes (electrical pulses).
• Compute (MRAM Synapses): Magnetic Tunnel Junctions (MTJs) in MRAM act as non-volatile synapses, storing neural network weights directly where they are used. This enables instant-on capability and near-zero standby power, emulating innate memory and reflexes.
• Compute (STNO Neurons): Spin-Torque Nano-Oscillators (STNOs) act as compact, high-frequency (GHz) spiking neurons. They can form reservoirs for processing temporal tasks and sensorimotor transformation, such as optic flow analysis.
• Actuation (RT Core): The output (set-points) of the spintronic SNN feeds a deterministic Real-Time Core (RT Core) that performs final control (PID/LQR) and drives the actuators.

4. Thermoregulation, Frequency Control, and “Natural Engine” Analogies

Small flyers sit on an unforgiving surface-to-volume battlefield: heat is generated by volume but lost through surface. We formalize the physics, then build an intuitive efficiency comparison among insects, ICEs, and miniturbines.

4.1. Discontinuous Gas Exchange (DGC) and Idle I/O Gating

At rest, many insects use DGC spiracles cycle Closed → Flutter → Open—to satisfy O₂ needs while minimizing water loss and oxidative stress [29,30,31,32,33,34,35,36]. We adopt DGC as I/O gating: Closed (deep idle), Flutter (duty-cycled health checks), Open (full bandwidth on demand).

4.2. Thermal Governance as a State Machine

Insects actively control temperature during any phase of operation. This need for active control highlights a key advantage over conventional micro-scale propulsion systems: Unlike internal combustion engines or miniature turbines, which are power-limited by narrow best-efficiency islands due to strict thermal and mechanical tolerances, the insect decouples output power (frequency) from efficiency by actively maintaining a near-constant thermal set-point across a broad operational load. This biological mechanism gives the system superior operational flexibility and near-constant efficiency across the entire performance range, a crucial goal for Edge-AI power management.

This insect-inspired thermal governance system formalizes burst budgeting as a predictive control loop that allows transient maximum power P_max to temporarily exceed steady-state thermal dissipation based on the explicit assumption of a lumped thermal capacitance model tau for the critical component cluster (the Reflex Island); the permissible burst duration t_burst is then dynamically calculated and corrected by a feedback term proportional to the current thermal debt ΔT_debt = T_core − T_setpoint and environmental variable correction factor, predominantly the ambient temperature T_amb, to ensure the critical component temperature T_crit is never exceeded.

In flight, insects actively thermoregulate: shivering warms thoracic muscles to a tight performance band; ventilatory/evaporative cooling and head–thorax heat partitioning hold neural function while exporting heat [33,34,35,36] Figure 7. We mirror this with a firmware state machine: REST → WARM-UP → WORK → BURST → COOL-DOWN → FAULT-SAFE, always preserving Reflex deadlines while shedding Policy first Figure 7.

4.3. Why Thermoregulation Tightens at Small Scale (Black-Body + Convection)

Small flyers sit on an unforgiving surface-to-volume battlefield: heat is generated by volume but lost through surface Figure 7. A lumped heat balance for a body at temperature $T$ is

$$C\hspace{0.17em}{\displaystyle \frac{dT}{dt}}=P_{\mathrm{gen}}-hA\hspace{0.17em}(T-T_{\mathrm{\infty }})-\epsilon \sigma A\hspace{0.17em}(T^4-T_{\mathrm{\infty }}^4)$$

with thermal capacitance $C=\rho cV$, convection coefficient $h$, emissivity $\epsilon$, and the Stefan–Boltzmann constant $\sigma$. Linearizing radiation about ambient $T_{\mathrm{\infty }}$ yields a combined cooling conductance:

$$G\hspace{0.25em}\hspace{0.17em}\approx \hspace{0.25em}\hspace{0.17em}hA\hspace{0.25em}\hspace{0.17em}+\hspace{0.25em}\hspace{0.17em}4\hspace{0.17em}\epsilon \sigma A\hspace{0.17em}{T}_{\mathrm{\infty }}^3$$

so the dynamics reduce to a first-order form $C\hspace{0.17em}\dot{T}=P_{\mathrm{gen}}-G\hspace{0.17em}\left(T-T_{\mathrm{\infty }}\right)$ with thermal time constant $\tau =C/G$. With characteristic length $L$, $A\propto L^2$ and $V\propto L^3\Rightarrow \tau \propto L$: smaller bodies (large $A/V$) heat and cool much faster, demanding active thermoregulation to hold performance set-points [37,38].

4.4. A Four-Stroke “Natural Engine” for Insect Thermoregulation

Insects do not have distinct strokes; they modulate continuously. We reframe insect thermal cycles using four-stroke terminology familiar to engineers, not to imply mechanical equivalence but to aid conceptual mapping to firmware state machines Figure 8, as follows:

• REST/IDLE (Intake)—DGC closes spiracles; minimal leaks [29,30,31,32].
• WARM-UP (Compression)—shivering thermogenesis raises thoracic T; head is kept cooler to protect timing [33,34,35].
• WORK/BURST (Power)—wing actuation couples to ventilation; evaporative cooling grows with work to export heat [34,35,36].
• COOL-DOWN (Exhaust)—ventilation/evaporation repay thermal debt; revert to REST when set-point recaptured [33,34,35,36].

• Sensing: Thoracic temperature (for power output) and ambient temperature are monitored.
• Compute (Control): A central control mechanism (analogous to a firmware state machine) actively regulates heat production (e.g., shivering) and heat dissipation (e.g., head cooling/evaporative cooling) to maintain a performance-optimal thoracic temperature set-point.
• Engineering Analogy: This is mirrored by an Edge-AI system’s thermal governance, which uses a state machine to dynamically adjust power/clock frequency (DVFS) and sensor duty cycles based on thermal sensors and predicted load, allowing for short, high-performance bursts while preventing thermal runaway.

4.5. Propulsion Analogy: Injection/Wingbeat Frequency and Efficiency

4.5.1. Injection Event Frequency in ICEs

In a four-stroke engine, each cylinder has one combustion event every two crank revolutions. If each event uses $N_{\mathrm{inj}}$ micro-injections, the per-cylinder injector command rate is

$$f_{\mathrm{inj},\mathrm{cyl}}\hspace{0.25em}\hspace{0.17em}=\hspace{0.25em}\hspace{0.17em}{\displaystyle \frac{\mathrm{RPM}}{120}}\hspace{0.17em}{N}_{\mathrm{inj}}[\mathrm{Hz}]$$

and for an even-fire engine with $n_c$ cylinders, the aggregate scheduling rate is

$$f_{\mathrm{inj},\mathrm{agg}}\hspace{0.25em}\hspace{0.17em}=\hspace{0.25em}\hspace{0.17em}{\displaystyle \frac{\mathrm{RPM}}{60}}\cdot {\displaystyle \frac{n_c}{2}}\cdot N_{\mathrm{inj}}[\mathrm{Hz}]$$

Examples. At 8000 rpm with $N_{\mathrm{inj}}=6$ and $n_c=4$: $f_{\mathrm{inj},\mathrm{cyl}}=8000/120\times 6=400 \mathrm{Hz}$ per cylinder; $f_{\mathrm{inj},\mathrm{agg}}\approx (8000/60)\times (4/2)\times 6\approx 1600 \mathrm{Hz}$. At 2000 rpm with the same $N_{\mathrm{inj}}$: $f_{\mathrm{inj},\mathrm{cyl}}\approx 100 \mathrm{Hz}$, $f_{\mathrm{inj},\mathrm{agg}}\approx 400 \mathrm{Hz}$. Thus modern multi-pulse strategies operate squarely in the ${10}^2$–${10}^3 \mathrm{Hz}$ decade at typical speeds [39,40,41,42,43].

Multiple injections (pilot/main/post; up to 5–8 pulses per event) [39,40,41,42,43] shape heat-release for emissions/noise without changing the thermodynamic island where efficiency peaks.

4.5.2. Wingbeat Frequency in Insects

Wingbeat falls in the same 10²–10³ Hz decade but is produced by asynchronous flight muscle and thorax resonance: neural spikes gate contractions while frequency stems from elastic mechanics, enabling high-Q operation with modest compute. Honeybees hover at ≈230 Hz [44,45,46,47], some small dipterans exceed 1 kHz [44,45].

4.5.3. Efficiency Maps: Narrow Islands vs. Near-Flat Bands

ICEs (and Brayton turbines) display narrow best-efficiency islands (low BSFC or high specific efficiency) at particular power–rpm combinations; moving away, especially to low load, induces pumping and heat losses. Adjusting injection/ignition timing or increasing pulse frequency does not flatten the thermodynamic map [39,48,49].

Insects, by contrast, regulate power primarily by wingbeat frequency and stroke while maintaining thoracic temperature near an optimal set-point Figure 9, so mechanical conversion efficiency varies only modestly across the functional range: frequency-based power, nearly constant efficiency [33,34,35,36,44,45,46,47]. Classical work-loop and respirometry studies [50,51,52] report mechanical efficiencies for flight muscle that fall in the ~10–15% range for several species and preparations, even as mechanical power output and wingbeat frequency vary by about a factor of three to four. Rather than attempt a precise meta-analysis of heterogeneous experiments, we summarize these data in Figure 10 as a shaded efficiency band between 10% and 15% across a broad span of normalized mechanical power. This stands in contrast to the narrow high-efficiency peak sketched for ICEs and miniturbines, whose efficiency falls off steeply away from the island.

Insect flight muscle does not have perfectly flat efficiency, but its load dependence over the biologically relevant operating range is modest compared to conventional heat engines. This justifies the “near-flat band” representation adopted in Figure 10 and underpins the propulsion analogy in Section 5, where we seek engineered systems whose prime movers behave more like insect flight muscle than like classical BSFC maps.

4.6. Miniturbines: Scaling Limits, High Rpm, and Cold-Start Latency

Miniaturization hurts Brayton machines: higher relative tip-clearance and lower Reynolds degrade stage efficiency; non-adiabatic effects loom larger [53,54,55,56]. Even commercial ~30 kW microturbines (e.g., Capstone C30) reach only ~26% [57] electrical efficiency (LHV) in recuperated simple cycle, far below big turbomachinery [57]. To reclaim pressure ratio at small diameters, rotors spin very fast (often >120 krpm) [58,59], stressing bearings/rotors and complicating thermal transients [58,59]. Start sequences (purge → light-off → accel) impose ~15–20 s [60] cold-to-idle delays even for RC-scale microjets, unlike the insect’s instant re-engagement once warm.

Consequence: Miniturbines show narrow efficiency peaks, seconds-scale startup, and rpm ceilings, demanding strict burst budgeting and explicit warm-up/cool-down policies.

4.7. Computational Governance: Engine ECUs vs. Insect Nervous Systems

Modern powertrains use ASIL-D MCUs delivering multi-billion instructions/s (DMIPS) paired with hardware timing engines for sub-ms injector/ignition scheduling, e.g., Infineon AURIX TC39x (~2700 DMIPS; six 300 MHz cores) and NXP MPC5777C (dual 300 MHz + eTPU2 channels for precise pulse timing) [13,61,62,63].

Insects achieve comparable actuation rates with distributed, event-driven control: a modest descending channel gates thoracic CPGs, while asynchronous muscle and elastic thoraces supply high-frequency mechanics [44,45,46,47,64]. The whole-brain Drosophila connectome (~139k neurons, ~50+ M synapses) underscores a rich yet compact controller that remains compatible with low-power edge implementations [65].

4.8. Firmware Hooks: Operationalizing Natural Strategies for Timing, Thermal, and Safety Engineering

Physics-aware burst budgets. From Section 4.3, model the critical component temperature with

$$C\hspace{0.17em}\dot{T}\hspace{0.25em}\hspace{0.17em}=\hspace{0.25em}\hspace{0.17em}{P}_{\mathrm{burst}}-G\hspace{0.17em}\left(T-T_{\mathrm{\infty }}\right), C=\rho cV \mathsf{\tau }=\mathrm{C}/\mathrm{G}$$

With initial $T(0)=T_0$, the solution is

$$\mathsf{\Delta }T(t)\equiv T(t)-T_0\hspace{0.25em}\hspace{0.17em}=\hspace{0.25em}\hspace{0.17em}{\displaystyle \frac{P_{\mathrm{excess}}}{G}}(1-e^{-t/\tau }){ P}_{\mathrm{excess}}=P_{\mathrm{burst}}-G\hspace{0.17em}(T_0-T_{\mathrm{\infty }}).$$

The allowable burst duration is the first $t_b$ such that $\mathsf{\Delta }T(t_b)=\mathsf{\Delta }{T}_{\mathrm{m}\mathrm{a}\mathrm{x}}=T_{\mathrm{m}\mathrm{a}\mathrm{x}}-T_0$, i.e.,

$$t_b\hspace{0.25em}\hspace{0.17em}=\hspace{0.25em}\hspace{0.17em}-\hspace{0.17em}\tau \hspace{0.17em}\mathrm{l}\mathrm{n}(1-{\displaystyle \frac{G\hspace{0.17em}\mathsf{\Delta }{T}_{\mathrm{m}\mathrm{a}\mathrm{x}}}{P_{\mathrm{excess}}}}).$$

After a burst, define thermal debt $D=T\left(t_{\mathrm{end}}\right)-T_{\mathrm{set}}$ and remain in COOL-DOWN until $D\to 0$; Reflex loops stay live while Policy sheds first.

• Scheduling guardrails. In WORK/BURST, lock kHz Reflex loops and shed Policy first as thermal limits near; in COOL-DOWN, down-clock but keep Reflex live—matching insect priorities [33,34,35,36].
• Sensing as “spiracles.” Treat thermistors/airflow sensors like spiracle baroreceptors; gate I/O per DGC and drive DVFS/ventilation set-points accordingly [29,30,31,32].

5. Use Cases and Actionable Guidance for Adoption

The insect-inspired architecture, latency-first, two-tier control with a Reflex Tier (near-sensor, kHz-rate, instant-on via MRAM/spintronic primitives) and a Policy Tier (slower planning/learning) plus the state-dependent thermal governance of Section 4, maps directly onto high-stakes systems that must act quickly, autonomously, and efficiently under tight energy and thermal limits. Below, we convert the biology and physics into engineering recipes, with concrete components, control patterns, and validation steps.

Use-case differentiation: the same Reflex/Policy split applies across domains, but the dominant constraints differ: (i) propulsion emphasizes burst power, thermal limits and actuator authority; (ii) micromobility emphasizes human-in-the-loop warnings, privacy, and very low average power; (iii) industrial robotics emphasizes uptime, deterministic fieldbus timing and certified safe-stop; and (iv) implants emphasize multi-year standby, biocompatible thermal limits and secure logging. We therefore treat the thruster and cargo e-bike as the main case studies, while additional domains are summarized as concise vignettes.

5.1. Design of Novel Fuel-Based Propulsion Systems with High Thrust (Make Power by Frequency—Keep Efficiency Flat)

As established in Section 4.5 and Section 4.6, ICEs and miniturbines are peak-island machines; insects achieve flat-band efficiency by frequency control + thermoregulation; therefore, we decouple the prime mover (held at its island) from the propulsor (frequency-controlled).

5.1.1. Objective and Rationale

Conventional thermal engines (ICEs and miniturbines) exhibit narrow best-efficiency islands tied to a particular power–rpm; efficiency falls sharply away from that point (see Figure 9; Section 4.5.3). Tuning injection pulse frequency and timing helps emissions and noise but does not flatten the fundamental efficiency map (Section 4.5.1, Section 4.5.2 and Section 4.5.3). Insects instead regulate power by actuation frequency while holding muscle efficiency nearly constant by keeping temperature at a set-point and scaling ventilation/evaporation with work (Section 4.2, Section 4.3, Section 4.4 and Section 4.5).

Goal: build fuel-based propulsion that behaves like an insect at the system level, frequency-controlled thrust with near-constant efficiency over a wide range, by decoupling the prime mover from the propulsor and letting each live in its optimum regime.

5.1.2. Architecture Blueprint (“Frequency Governor” Hybrid)

• Prime mover (constant-efficiency island): a small ICE or miniturbine run near its best-efficiency point (narrow island; Section 4.5.3 and Section 4.6).
• Electric buffer: battery + supercaps sized to absorb burst gaps between average and instantaneous power (Section 4.3 ODE + burst budgets).
• Propulsor (frequency-controlled): high-response electric motor + prop/fan/pump-jet; thrust tracks motor electrical frequency, keeping mechanical efficiency high across output range (insect analog).
• Power electronics: bi-directional DC/DC + inverter; reflex-class PWM at kHz.
• Reflex Tier: MRAM-resident, instant-on controller executing stabilization and thrust commands at kHz (mirrors thoracic reflex loop).
• Policy Tier: slow planner (routing, mission, optimization), exporting only goal states.
• Thermal governance: the state machine from Section 4.2 enforces WARM-UP → WORK → BURST → COOL-DOWN with thermal-debt accounting from Section 4.3.

Why it works: the prime mover sits on its island almost all the time; thrust is modulated by motor frequency, just like wingbeat in Section 4.5.2. The buffer covers fast transients and manages the latency of thermal machines (Section 4.6: miniturbines’ cold-to-idle seconds).

5.1.3. Control Strategy (Reflex/Policy Split)

Reflex (kHz): Close thrust/torque and stabilization loops within 1–2 ms end-to-end. Enforce the thermal burst budget by integrating $C\hspace{0.17em}\dot{T}=P_{\mathrm{burst}}-G\hspace{0.17em}(T-T_{\mathrm{\infty }})$ online and terminating BURST when $T$ reaches $T_{\mathrm{m}\mathrm{a}\mathrm{x}}$ or when the closed-form limit $t_b=-\tau \mathrm{l}\mathrm{n}\left(1-{\displaystyle \frac{G\hspace{0.17em}\mathsf{\Delta }{T}_{\mathrm{m}\mathrm{a}\mathrm{x}}}{P_{\mathrm{excess}}}}\right)$ is hit; log thermal debt and transition to COOL-DOWN while keeping Reflex deadlines met. Shed Policy first whenever thermal/voltage margins shrink.

Policy (10–100 ms+): Keep the prime mover on its iso-BSFC island with slow trims; plan missions and schedule WARM-UP before expected BURST windows.

5.1.4. Hardware Patterns by Domain

• Road (range-extender)

• A 20–60 kW ICE or microturbine @ island → 400–800 V DC link; 200–400 kWh battery + cap bank sized for bursts.
• Propulsor: axle motors with field-oriented control (FOC); thrust = torque request = frequency (electrical).
• Outcome: city stop-go handled by the buffer + frequency control (insect-like), engine sits at steady island → flat real-world efficiency.

• Air (distributed electric propulsion/hybrid)

• A 30–150 kW miniturbine-generator at island (cf. Section 4.6); high-rpm and start latency hidden by buffer.
• Multiple small props per wing; per-motor frequency sets local thrust, enabling gust rejection with Reflex kHz loops.
• Outcome: safe, responsive thrust modulation without throttling the turbine off-design.

• Water (pump-jet/prop)

• Diesel-gen at island; pump-jet thrust from impeller frequency; cavitation avoided via Reflex watchdogs (pressure/accel).
• Outcome: smooth low-speed thrust with prime mover steady; excellent station-keeping.

5.1.5. Sizing and Math Hooks (Quick Rules)

Energy buffer. For a target burst window $\mathsf{\Delta }t$, size the buffer so that

$$E_{\mathrm{usable}}\hspace{0.25em}\hspace{0.17em}\ge \hspace{0.25em}\hspace{0.17em}{E}_{\mathrm{burst}}\approx (P_{\mathrm{burst}}-P_{\mathrm{avg}})\hspace{0.17em}\mathsf{\Delta }t$$

at end-of-life conditions (hot, aged).

Thermal limit. Use Section 4.3 parameters to compute the allowable burst time

$$t_b\hspace{0.25em}\hspace{0.17em}=\hspace{0.25em}\hspace{0.17em}-\hspace{0.17em}\tau \hspace{0.17em}\ln \left(1-{\displaystyle \frac{G\hspace{0.17em}\mathsf{\Delta }{T}_{\mathrm{m}\mathrm{a}\mathrm{x}}}{P_{\mathrm{excess}}}}\right), \tau ={\displaystyle \frac{C}{G}}, { P}_{\mathrm{excess}}=P_{\mathrm{burst}}-G\hspace{0.17em}\left(T_0-T_{\mathrm{\infty }}\right).$$

Thrust scaling reminder. For props/fans at similar efficiency and fixed geometry, thrust follows shaft frequency; regulate frequency (insect analog) rather than throttling off-island.

5.1.6. Safety and Certification Hooks

The insect-style separation between fast Reflex and slower Policy provides explicit hooks for safety cases under ISO 26262 [66], IEC 61508 [67], or ISO 13849 [68]. For a given propulsion application, we adopt the following:

• Freedom-from-interference (FFI). The Reflex Tier runs on a pinned core (or dedicated neuromorphic die) with fixed priorities, no dynamic memory, and one-way single-producer/single-consumer (SPSC) queues from Policy. All plant-stabilizing loops (thrust, torque, braking) close entirely inside this island. Policy cannot pre-empt or delay Reflex work; its influence is limited to low-rate goal states (e.g., speed corridors, thrust limits).
• WCET envelopes. For each Reflex loop, we derive a WCET budget as in Table 4, starting from sensor exposure through neuromorphic layers to PWM/FOC update. For propulsion, we target <5 ms end-to-end stabilization, with critical safeties < 1 ms. Implementation-specific WCETs are taken at the hot corner (max temperature, min supply) and must exhibit ≥30% margin versus the requirement (e.g., 3.4 ms vs. 5 ms).
• Thermal and prime-mover monitors. Die/EGT sensors feed Reflex logic that asserts COOL-DOWN when thermal debt exceeds the budget (Section 4.2 and Section 4.3). Prime-mover faults (turbine overspeed, EGT slew) trigger fuel-shed while the Reflex Tier keeps the vehicle stable on the buffer, providing a documented graceful degradation path.
• Fault campaigns. Section 5.3 details the fault taxonomy and injection methodology: transient single-event upsets (SEUs) in MRAM, STNO phase/amplitude jitter, sensor dropouts, and actuator faults are injected while verifying deadline compliance, safe-state entry latency, and bounded output deviation versus a fault-free trace. These campaigns, run on HIL rigs for the propulsion plant, provide the quantitative evidence required for SIL 2–3/ASIL-C–D allocations.

5.1.7. Validation Plan (Step-by-Step)

• HIL loop at kHz with plant models (prop, drivetrain, buffer, engine/turbine).
• Burst tests: record $\left(P,T,\mathrm{R}\mathrm{P}\mathrm{M}\right)$ vs. predicted Section 4.3 curve; verify deadlines and miss counters = 0.
• Thermal cycles: alternate BURST/COOL-DOWN to validate thermal-debt controller.
• Off-design trials: hold the prime mover at the island while sweeping the thrust frequency; confirm flat system efficiency compared to stock throttle maps.
• Fault injection: sensor dropout, clock drift, memory CRC fault; verify Reflex containment.

5.1.8. Quick “Principle → Requirement → Action”

A quick mapping from biological principles to propulsion requirements and concrete implementation actions is summarized in

Table 5. Principle → propulsion requirement → actionable guidance mapping for the proposed architecture.

Principle	Propulsion Requirement	Actionable Guidance
Frequency-controlled thrust (insect)	Wide-range thrust with minimal efficiency drift	Decouple: prime mover at island; thrust via motor frequency; buffer covers transients (Section 4.5 and Section 4.6)
Thermal governance	Bounded temps during bursts	Apply Section 4.3 ODE to compute burst time; enforce COOL-DOWN; shed Policy first
Instant-on Reflex	kHz stabilization independent of prime-mover state	MRAM Reflex Island near drivers/sensors; deadline monitors; DMA windows
Cold-start latency (miniturbine)	Usable thrust before light-off	Pre-warm plan; buffer-only takeoff/launch; soft-hand-over to turbine (Section 4.6)
Safety case	WCET & FFI	Fixed priorities; single-core pinning; one-way SPSC queues; watchdogs and logs

.

5.1.9. Insect-Inspired Fuel-Based IFEVS Thruster

Building on the experience of the first author, who, as former director of the Fiat Research Centre, contributed to the development and industrialization of the common-rail diesel injection concept, one of the key enablers in raising production diesel engine efficiencies from ~25% to >45%, we now seek an analogous step change in specific efficiency for small air-breathing thrusters. The IFEVS concept is in an advanced design and prototyping phase; the figures used here are engineering estimates informed by subsystem tests and 1D cycle analysis. A separate, dedicated propulsion paper is in preparation; the present section only needs enough fidelity to support the architectural argument of Section 5.1.

Data Status, Test Conditions, and Methodology

The quoted >40% thermal efficiency refers to fuel-to-jet power at the outlet of the hot core before the compact augmenter. This value is obtained from a cycle model calibrated against component-level measurements of combustor pressure loss and temperature rise, nozzle efficiency, and measured mass flow under ISA sea-level static conditions (ambient 15–20 °C, p ≈ 101 kPa, zero flight speed). The working fuel in all comparisons is Jet-A/kerosene with a lower heating value LHV ≈ 43 MJ/kg; we assume ±1 MJ/kg variation in LHV in the uncertainty budget.

The present prototypes are instrumented with conventional laboratory sensors (differential and absolute pressure transducers, K-type thermocouples, and fuel flowmeters), but a fully integrated, long-duration test rig with formal calibration traceability is still under construction. For this reason, we treat the IFEVS numbers here as design-point values with conservative uncertainty bands, rather than as final certified performance. We report thermal efficiency as 40% ± 5 percentage points, reflecting model uncertainty, instrumentation accuracy, and sensitivity to assumed pressure losses.

Reference Microturbine and Comparison Basis

To make the comparison concrete, we benchmark against a commercial ~400 N-class microturbine (JetCat-P400), using public thrust and fuel-flow data across its allowable throttle range. From these we derive 10 representative points between 80 N and 540 N equivalent thrust. At each point, we compute the effective fuel consumption (mL/min) and an approximate overall thermal efficiency using the same Jet-A LHV and sea-level static assumptions.

On the IFEVS side, the cycle model provides core mass flow, temperature ratio, and jet power as a function of fuel flow, from which we derive thrust before and after the compact augmenter. This augmenter trades jet speed for entrained mass flow and static pressure recovery, yielding a thrust multiplication of 1.8 ± 0.1 with only a modest additional pressure loss. The result is a nearly flat efficiency band from ~30 N to >600 N net thrust, in contrast to the narrow peak of the microturbine.

A summary comparison is given in

Table 6. IFEVS vs. JetCat P400 fuel flow vs. net thrust (ISA SL, Jet-A).

Thrust (N)	IFEVS Fuel (mL/min)	P400 Fuel (mL/min)	Fuel Ratio IFEVS/P400
80	143	391	0.37
120	215	507	0.42
160	287	623	0.46
200	358	739	0.48
240	430	855	0.50
280	502	971	0.52
320	573	1087	0.53
360	645	1203	0.54
400	717	1319	0.54
440	788	—	—
480	860	—	—
520	932	—	—
540	968	—	—

JetCat P400 values are only shown up to 400 N, consistent with the manufacturer-rated thrust band; no extrapolation is used, so cells above 400 N are left blank.

, where for each thrust level we list the following:

• Microturbine thrust and fuel flow;
• IFEVS thrust and fuel flow;
• The resulting fuel-flow ratio IFEVS/microturbine.

Over the 400 N design point, the IFEVS thruster consumes ≈ 0.5 ± 0.05 times the fuel of the reference microturbine. At low partial load (~80 N), where the turbine operates far off its BSFC island, the ratio drops to ≈0.37 ± 0.05, consistent with the insect analogy of nearly constant muscle efficiency over a wide power range (Section 4.5.3) Figure 11.

In practice, the IFEVS thruster behaves like an internal combustion engine that never leaves its best-efficiency island Figure 11: it reaches a typical ICE peak thermal efficiency (≈40%) but, insect-style, keeps this efficiency essentially flat across its whole operating thrust range, rather than collapsing at partial load as miniturbines do.

• Uncertainties, limitations, and mission-level projections

• The uncertainties quoted above are engineering confidence intervals derived from the following:
• Model sensitivity to assumed pressure losses and mixing efficiency (±3–4 percentage points on thermal efficiency);
• Expected calibration accuracy of mass-flow and temperature sensors (±2–3%);
• Variability in augmenter entrainment ratio and back-pressure (±0.05 on thrust multiplication);
• Variability in ambient conditions around the ISA reference (±5% in density over a 10–15 °C swing).

Combining these conservatively gives the ±5 percentage points band around the 40% thermal-efficiency figure and the ±0.05 bands on the fuel ratios quoted above. For acoustic predictions, we similarly carry a ≤100 ± 3 dB at 3 m estimate, based on subsonic ejector exit velocities and standard semi-empirical jet-noise models; full aeroacoustic measurements are part of the planned integrated test campaign.

At the system level, we have also simulated simple mission profiles, hover, climb, and level cruise, using the same cycle model coupled to a notional vehicle mass and drag. These simulations show that, for representative duty cycles, the integrated fuel saving versus the microturbine remains in the 40–60% range, with the largest relative gains appearing at low-power segments (hover, loiter) where the turbine spends long periods far from its efficiency island. Because the mission simulations share the same model and assumptions as the static design points, we present them qualitatively and defer full time-series plots of thrust, fuel flow, exhaust gas temperature, and exhaust temperature to a dedicated technical report.

• Architectural implications

• Even with these conservative uncertainties, the picture that emerges is robust (

  Table 7. Comparison table of the insect-inspired fuel-based thruster vs. state-of-the-art microturbine.

  Metric	IFEVS Thruster	State-of-the-Art Microturbine (Similar Thrust)
  Thermal efficiency (core)	>40% before augmenter (fuel→jet power)	~20–30% peak; sharp drop-off design
  Thrust–load behavior	Flat efficiency from ~30 N → 600+ N after augmenter	Narrow island; poor partial-load BSFC
  Fuel use @ ~400 N	≈½ the fuel of miniturbine	Baseline
  Fuel use @ ~50 N	≈⅓ the fuel vs. turbine at low-load operation	Strong efficiency loss at low throttle
  Exhaust temperature	~150 °C at augmenter exit	~500–1000 °C EGT; hot jet
  Acoustic signature	≤100 dB @ 3 m, subsonic ejector exit	Hot, often supersonic microjets; much louder
  Response time	Near-instant; no spool-up	Spool-up/light-off delays (seconds)
  Architecture/maintenance	No rotating parts; ~½ weight; low maintenance	High-speed rotor/bearings; higher maintenance load

  ):
• An insect-style prime mover whose efficiency degrades gently with load instead of collapsing away from a narrow island;
• Instantaneous response (valve/ignition limited) rather than seconds-scale spool dynamics;
• Cool, slow exhaust (~150 °C at the augmenter exit) enabling safe operation near people and structures; and
• A mechanically simple, non-rotating architecture naturally compatible with the Reflex/Policy split and WCET reasoning of Section 2 and Section 5.1.6.

The IFEVS thruster is a worked example of how insect-inspired thermodynamics and neuromorphic control can combine: a compact “thorax” running near its efficiency sweet spot, coupled to an ejector “wing” that shapes thrust without paying the usual penalties at partial load (Table 7). A full validation and peer review of the propulsion data will follow in a dedicated venue; here, the thruster serves to make the architectural trade-offs of the insect template concrete.

Efficiency and fuel use. At the thermodynamic level, the IFEVS core reaches >40% thermal efficiency before the compact augmenter, i.e., roughly double the typical 20–30% peaks reported for small recuperated microturbines (Section 4.6). Because thrust is boosted by an ejector-style augmenter that trades jet speed for entrained mass flow and static pressure recovery, the system maintains a near-flat efficiency band from ~30 N to >600 N net thrust. This directly mirrors the insect pattern of “power by frequency at nearly constant efficiency” (Section 4.5.3): thrust is modulated by flow and frequency, not by throttling the core far off its island. In practical terms, at a ~400 N design point, the IFEVS thruster consumes roughly half the fuel of a miniturbine, and at ~50 N partial load, the fuel burn can drop to about one-third of a turbine operating at the same net thrust but far from its optimum island.

Augmenter, exhaust temperature, and safety. The compact augmenter delivers an additional ~×1.8 thrust boost with minimal extra fuel by entraining ambient air and recovering static pressure, rather than simply accelerating a small hot jet. This produces a much cooler mixed exhaust, with gas temperatures ~150 °C at the outlet instead of the 500–1000 °C typical for microturbine exhaust. The resulting low-temperature, low-velocity jet greatly simplifies vectoring hardware, reduces thermal loading on nearby structures, and shrinks the IR signature, critical for operations close to personnel, sensitive payloads, or in contested environments. In the insect analogy, the hot “muscle” is kept compact while the effective “wing” is a much larger mass of cooler entrained air.

Noise, signature, and mission envelopes. Because the ejector exit remains subsonic and the jet is relatively cool, predicted acoustic levels are ≤100 dB at 3 m for representative layouts, dramatically below the harsh tonal noise produced by hot, sometimes choked microjets of similar thrust. Combined with the low exhaust temperature, this softens both acoustic and infrared signatures, enabling operations near ground troops, in urban canyons, or for covert ISR platforms where microturbine noise and plume would be unacceptable. The low-speed gases are also naturally compatible with distributed ejector arrays, enabling rotor-free VTOL matrices where lift comes from many small, cool jets rather than exposed high-tip-speed rotors.

Response time and Reflex compatibility. Microturbines suffer from cold-to-idle and spool-up latencies in the 10–20 s range (Section 4.6), forcing them to loiter at fuel-wasting idle whenever immediate thrust might be required. The IFEVS thruster, with no high-inertia rotating assembly, offers near-instant activation: thrust is gated primarily by fast valves and ignition rather than rotor acceleration. This matches the latency-first architecture of Section 2: a neuromorphic Reflex Tier can safely command rapid starts, hops, and burst thrust changes within the kHz sensor–actuator loop, without being constrained by seconds-scale turbomachinery transients. In other words, the effective cold-to-useful-thrust latency becomes compatible with insect-like bursts and with the burst-budgeting logic of Section 4.2 and Section 4.3.

System simplicity, VTOL potential, and business appeal. Compared to a miniturbine, the IFEVS thruster is mechanically simple (no compressor or turbine stages, no high-speed bearings), exhibits higher intrinsic reliability, and is expected to weigh roughly half as much for the same thrust envelope. The cold, low-speed exhaust opens the door to closely packed, ducted thruster matrices for rotorless VTOL and agile attitude control, directly benefiting from the frequency-governed thrust patterns of Section 5.1. From an industrial standpoint, the architecture is compatible with automotive-style manufacturing (sheet metal, castings, standard injectors), supports multiple fuels including future green drop-ins, and scales from small logistics drones to larger platforms. This combination of deep-tech novelty (spintronic-enabled Reflex control, insect-inspired thermodynamics) with a clear manufacturing and certification path is precisely the kind of defensible, low-risk innovation that can appeal even to conservative investors and defense procurement agencies.

5.2. Autonomous Vehicles and Micro-Robotics (A One-to-One Mapping of the Insect Edge)

A one-to-one mapping from insect Edge-AI principles to autonomous-vehicle and micro-robotics requirements is summarized in

Table 8. One-to-one mapping of insect Edge-AI principles to autonomous-vehicle and micro-robotics requirements, with actionable engineering guidance.

Principle	Vehicle Requirement	Actionable Guidance
Reflex Tier (Fly)	Deterministic safety: stabilization, obstacle avoidance, traction/braking	Co-locate IMU/event camera + Reflex Island; run stabilization at 1–2 kHz; keep E-stop < 1 ms path; MRAM for instant-on
Policy Tier (Bee)	Robust navigation: VO, mapping, intent	Separate RT core/NPU at low priority; publish only goal states (speed corridors, waypoints) to Reflex
Thermal governance	Predictive performance: short high-power bursts	Implement Section 4.2 state machine; compute burst budgets via Section 4.3; log thermal debt and deadline misses
Spintronics	Low-power, event-driven perception	Use STNO reservoirs for vibration/flow/event-camera streams; MRAM synapses for reflex weights
Certification	Freedom-from-interference	Partition clocks/cores/memory; lock-free SPSC; WCET tables; watchdog + safety log

Notes: For aerial micro-robots, Section 5.1′s frequency-governed thrust (electric props) is directly applicable; for ground robots, use frequency-to-torque via FOC current loops with the same thermal budgeting logic.

. The table links each biological principle to a concrete system requirement and an actionable implementation guideline.

Insect-Inspired Vision for Safer Cargo e-Bikes

Urban cargo e-bikes operate in a cluttered, high-conflict environment: dooring, side-swipes by vans, pedestrians stepping out from occlusions, and sudden braking by leading vehicles. Following recent work on active vision and pattern recognition in bees [69], insect-inspired neuromorphic computing [70], and insect-brain-inspired policy learning [71], we treat the bike and rider as a “fly + bee” system on wheels: wide-field, low-latency vision for immediate collision avoidance (fly), combined with slower heuristics and route-level support (bee).

Concretely, we propose a multi-camera “vision belt” around the cargo e-bike frame (front, rear, lateral fisheye modules) plus IMU and wheel-speed sensors. Each camera/IMU pair feeds a near-sensor Reflex Tier on a neuromorphic/low-power SoC, running insect-like primitives: wide-field optic-flow estimation, looming/closing-speed detection, lane/door-zone detection, and basic vulnerable-road-user classification, echoing the spatiotemporal encoding strategies observed in bee lobula neurons [69]. The Reflex Tier must signal imminent collision risks within a strict latency envelope so that the rider’s braking and evasive actions remain effective.

• Latency budget for the cargo e-bike Reflex loop

Table 9. Example Reflex-loop latency budget (cargo e-bike, front looming detection).

Stage	Function	Latency (ms)	Cumulative (ms)
1	Camera exposure + readout/event aggregation	5	5
2	DMA + timestamp + spike/event encoding into Reflex Island	2	7
3	Looming-detection SNN inference (1–2 layers, few k neurons)	4	11
4	Reflex decision logic + hazard classification + queue write	3	14
5	Haptic/LED driver update + actuator rise time	4	18

summarizes a representative latency budget for one Reflex loop (front camera + IMU → looming SNN → haptic/LED cue). We assume a 200–250 Hz global-shutter CMOS or DVS camera, with events delivered to a Loihi- or Akida-class neuromorphic core and a simple microcontroller-based haptic/LED driver. The numbers are worst-case at the hot electrical corner (high temperature, low voltage) and include guard bands.

This yields an end-to-end Reflex latency of ≈18 ms against a 20 ms design target, leaving ~10% margin. Immediate emergency cues (hard looming, imminent lateral impact) are generated directly by the Reflex Tier and bypass the slower Policy Tier, which handles trajectory-level reasoning. Given that human reaction times are typically 500 ms, this sub-20 ms envelope is sufficient to meaningfully extend the effective braking distance margin in urban traffic [72].

• Energy budget and solar sizing

From a power perspective, the cargo e-bike behaves like a small ground “drone”: propulsion dominates the energy budget, but sensing/compute must remain compatible with a largely self-charging vehicle. Reviews of electric cargo cycles report energy consumption in the range of 9–18 Wh/km depending on load and terrain; we adopt 14 Wh/km as a conservative value for loaded last-mile delivery work. For a typical urban duty cycle of 40 km/day, the propulsion demand is then about 560 Wh/day.

The neuromorphic safety shell (multi-camera belt, IMU, Reflex Tier, haptics/LEDs, and a small Policy MCU) can be dimensioned to draw on the order of 5 W average while riding and ≤1 W in idle/parked monitoring, using near-sensor event cameras and low-power SNN hardware. For 3 h/day of active riding and 9 h/day of low-rate monitoring, this corresponds to ≈30 Wh/day, small compared to propulsion but non-negligible in a solar-assisted system.

We therefore size a vehicle-integrated PV surface on the cargo box and canopy of A ≈ 1.0 m², consistent with existing solar e-bike and cargo-bike implementations (1.0–1.5 m² roofs or box faces). Assuming commercially available lightweight modules with η ≈ 23% [73] and a central south Europe latitude average insolation of ≈4.6 kWh/m²/day over the year on a horizontal surface, the daily energy harvest is

E_PV ≈ A × η × H × f ≈ 1.0 m² × 0.23 × 4.6 kWh/m²/day × 0.6 ≈ 0.63 kWh/day

where $f=0.6$ accounts for MPPT/charging efficiency and shadowing.

Compared to the ~0.63 kWh/day of total demand (0.60 kWh propulsion + 0.03 kWh sensing/compute), the PV system covers most km in a typical European summer/spring duty cycle, leaving a modest margin for less favorable conditions [74]. This order-of-magnitude balance is consistent with recent measurements on solar electric cargo bikes developed by IFEVS within the Edge-AI Trust project [75], whose ~210 Wp of integrated PV supply roughly 50% on average and up to 100% of the daily energy consumption in last-mile delivery operations of a 1000 L box cargo bike (full dataset available in forthcoming project report).

• Integration with the insect template

The resulting architecture is directly compatible with the insect template of this paper as follows:

• A fast Reflex Tier (camera/IMU → neuromorphic looming SNN → haptic/LED) with a quantified sub-20 ms WCET envelope and clear separation from the slower route-planning Policy Tier;
• A solar-governed duty cycle where the bike operates much like an insect, balancing energy intake and expenditure over the day; and
• A largely self-charging cargo platform analogous to existing IFEVS-type systems, but with an explicit safety-critical perception shell inspired by insect vision.

Additional domain vignettes (e.g., implants) are provided in Appendix A to keep the main narrative focused.

5.3. Industrial Robotics and Control (Uptime and Determinism Above All)

To achieve safety certification (e.g., ISO 26262/ASIL, IEC 61508), the Reflex Tier must be deterministic under both nominal operation and defined fault modes

Table 10. Insect-inspired Edge-AI principles mapped to industrial robotics requirements and actionable guidance for deterministic, certifiable control.

Principle	Industrial Requirement	Actionable Guidance
Reflex isolation	Immediate safety (E-stop, limits, interlocks)	Put all safety loops on Reflex Island; pin core, fixed priorities; zero dynamic memory; deadline monitors
Two-tier split	Flexible tasks w/o jitter	Reflex: 1–2 kHz PID/LQR, commutators; Policy: vision, optimization, job scheduling; one-way queues
STNO reservoirs	High-bandwidth sensing	Inline temporal processing for accelerometers/vibration; predictive maintenance at the edge
Thermal governance	Shift-resilience	Use Section 4.3 budgets; enforce COOL-DOWN; rate-limit bursts; log thermals for maintenance
Certification	SIL 2–3/ISO 13849	Prove freedom-from-interference; margin ≥ 30% on Reflex deadlines at hot corner; watchdog and safe states documented

. Concretely, we propose: (i) an explicit neuromorphic fault model (MRAM bit upsets/stuck-at MTJs, oscillator drift/jitter, spike-drop/extra-spike events, sensor burst overload) and systematic fault injection at the spike-stream and memory levels; (ii) hard WCET and spike-rate envelopes for the Reflex loop (including worst-case sensor event rates); (iii) safety filters and runtime monitors that enforce invariants (e.g., barrier-style guards) even if Policy outputs are corrupted; and (iv) a lightweight FMEA/FFI that traces each fault to bounded actuator impact and safe-stop behavior, validated first in simulation and then in hardware-in-the-loop with representative sensors and actuators.

6. Conclusions

We have argued that insects offer a compact but powerful blueprint for edge intelligence: a fast, latency-bounded Reflex Tier co-located with sensors and actuators, and a slower Policy Tier that reasons over maps, goals, and long timescales. On the hardware side, we showed how spintronic primitives (MRAM synapses and STNO “neurons”) can implement this hierarchy with predictable worst-case execution times, extremely low standby power, and innate robustness to noise. On the system side, we instantiated this template in two domains: an insect-inspired IFEVS thruster with nearly flat-band efficiency, and a 360° vision shell for solar cargo e-bikes. In both cases, the insect metaphor is not decorative, but structural: power is modulated primarily by frequency, thermal debt is carefully governed, and reflexes are strictly separated from higher-level policy.

From an environmental standpoint, the architecture is deliberately biased towards doing more with less. The IFEVS thruster operates at higher thermal efficiency than reference miniturbines across a wide load range, roughly halving fuel consumption around its 400 N design point and gaining even more at partial load. That translates directly into reduced CO₂ emissions and lower local pollutants for a given mission profile, particularly in duty cycles dominated by loiter, hover, or low-power cruise. The cargo e-bike example pushes the same logic to the urban ground scale: a safety-critical neuromorphic shell that typically consumes only tens of Wh per day and can be powered, together with propulsion, by a modest square meter of high-efficiency PV. Combined with near-sensor computing and spintronics, this suggests a path to energy-autonomous edge systems that lean on local renewables instead of continuous grid or cloud backhaul.

The social and ethical implications are equally important. Both application examples target safety in human environments: quieter, cooler thrusters that can operate near people and infrastructure without dangerous exhaust plumes, and cargo cycles that watch for dooring, side-swipes, and vulnerable road users. At the same time, the same sensing and control capabilities are intrinsically dual-use: the architecture that makes a delivery bike safer also makes a small drone more capable in contested space. It is therefore crucial that insect-inspired Edge AI be developed under explicit governance and transparency constraints: clear separation between safety reflexes and mission logic; documented WCET envelopes and fault-containment behavior; and strict data-minimisation for vision systems in public space (e.g., optic-flow and looming detection without long-term identity tracking). The insect blueprint helps here as follows: minimal memory, task-specific perception, and short retention by design.

There are, however, limits and risks on the hardware side, especially for spintronics. While embedded MRAM is already at high technology readiness and can be exploited as a non-volatile synaptic substrate, large-scale STNO reservoirs and dense spin-neuron fabrics are still maturing. Process variability, thermal noise, coupling complexity, and integration with CMOS place practical bounds on the size and precision of neuromorphic networks one can implement in the near term. Materials and supply-chain constraints (e.g., reliance on specific alloys or fabrication steps) may also cap scalability or concentrate manufacturing in a small number of fabs, which raises resilience and sovereignty questions. There is a risk of over-promising if spintronics is treated as a drop-in replacement for all forms of AI acceleration rather than as a targeted solution for ultra-low-power, reflex-like tasks.

More broadly, the insect template comes with architectural risks if misapplied. A reflex-dominated system without sufficient transparency can be difficult to audit or override; conversely, an overgrown Policy Tier that continuously interferes with Reflex loops undermines the very determinism the architecture is meant to guarantee. In this sense, the main limitation is not only technological but organizational: certification regimes, development processes, and human–machine interfaces must keep the Reflex/Policy boundary clean, explainable, and testable. Our proposed WCET envelopes, fault taxonomies, and fault-injection campaigns are first steps, not final answers.

Despite these caveats, we believe that “Edge AI in nature” is more than a metaphor: it is a design discipline. Insects show that it is possible to combine strict energy budgets, hard real-time reflexes, and surprisingly rich behavior in tiny, noisy packages. Translating that discipline into spintronic-enabled electronics, fuel-efficient thrusters, and solar-assisted micromobility will require sustained work across biology, materials science, safety engineering, and policy. If done carefully, the payoff is compelling: edge systems that are not only more capable, but also more frugal, quieter, safer, and easier to certify; in short, machines that borrow not just the agility of insects, but also their coexistence with the environments and societies they inhabit.