Journal of Low Power Electronics and Applications — Open Access Journal

The transconductance-to-drain-current method is a transistor sizing methodology that is commonly used in CMOS technology. In this study, we explored by means of simulations, a case of study and three figures of merit used for the method, and we conclude for the first time that the method should be reformulated. The study has been performed on Ultra-Thin Body and Buried Fully Depleted Silicon-On-Insulator 28 nm low-voltage-threshold NFET commercial technology (UTBB FD-SOI), and the simulations were performed via Spectre Circuit Simulator, by using the device model-card. To our knowledge, no previous attempts have been made to assess the method capability, and we collected very important results that infer that the method should be reformulated or considered incomplete for use with this technology, which has an impact and ramifications on the field of process modeling, simulation and circuit design. Full article

Aggressive power supply scaling into the near-threshold voltage (NTV) region holds great potential for applications with strict energy budgets, since the energy efficiency peaks as the supply voltage approaches the threshold voltage (V_T) of the CMOS transistors. The improved silicon energy efficiency promises to fit more cores in a given power envelope. As a result, many-core Near-threshold computing (NTC) has emerged as an attractive paradigm. Realizing energy-efficient heterogenous system on chips (SoCs) necessitates key NTV-optimized ingredients, recipes and IP blocks; including CPUs, graphic vector engines, interconnect fabrics and mm-scale microcontroller (MCU) designs. We discuss application of NTV design techniques, necessary for reliable operation over a wide supply voltage range—from nominal down to the NTV regime, and for a variety of IPs. Evaluation results spanning Intel’s 32-, 22- and 14-nm CMOS technologies across four test chips are presented, confirming substantial energy benefits that scale well with Moore’s law. Full article

The development of Internet of Things (IoT) systems is a rapidly evolving scenario, thanks also to newly available low-power wide area network (LPWAN) technologies that are utilized for environmental monitoring purposes and to prevent potentially dangerous situations with smaller and less expensive physical structures. This paper presents the design, implementation and test results of a flood-monitoring system based on LoRa technology, tested in a real-world scenario. The entire system is designed in a modular perspective, in order to have the capability to interface different types of sensors without the need for making significant hardware changes to the proposed node architecture. The information is stored through a device equipped with sensors and a microcontroller, connected to a LoRa wireless module for sending data, which are then processed and stored through a web structure where the alarm function is implemented in case of flooding. Full article

Abnormal foot postures can be measured during the march by plantar pressures in both dynamic and static conditions. These detections may prevent possible injuries to the lower limbs like fractures, ankle sprain or plantar fasciitis. This information can be obtained by an embedded instrumented insole with pressure sensors and a low-power microcontroller. However, these sensors are placed in sparse locations inside the insole, so it is not easy to correlate manually its values with the gait type; that is why a machine learning system is needed. In this work, we analyse the feasibility of integrating a machine learning classifier inside a low-power embedded system in order to obtain information from the user’s gait in real-time and prevent future injuries. Moreover, we analyse the execution times, the power consumption and the model effectiveness. The machine learning classifier is trained using an acquired dataset of 3000+ steps from 6 different users. Results prove that this system provides an accuracy over 99% and the power consumption tests obtains a battery autonomy over 25 days. Full article

A low-noise instrumentation amplifier dedicated to a nano- and micro-electro-mechanical system (M&NEMS) microphone for the use in Internet of Things (IoT) applications is presented. The piezoresistive sensor and the electronic interface are respectively, silicon nanowires and an instrumentation amplifier. To design an instrumentation amplifier for IoT applications, different trade-offs are discussed like power consumption, gain, noise and sensitivity. Because the most critical noisy block is the amplifier, a delay-time chopper stabilization (CHS) technique is implemented around it to eliminate its offset and 1/f noise. The low-noise instrumentation amplifier is implemented in a 65-nm CMOS (Complementary metal–oxide–semiconductor) technology. The supply voltage is 2.5 V while the power consumption is 0.4 mW and the core area is 1 mm². The circuit of the M&NEMS microphone and the amplifier was fabricated and measured. From measurement results over a signal bandwidth of 20 kHz, it achieves a signal-to-noise ratio (SNR) of 77 dB. Full article

Two new electronic tuning current-mode square-wave generators are introduced in the ensuing paper. In the first proposed square-wave generator circuit, one Operational Trans-resistance Amplifier (OTRA) and two passive components are involved, along with two NMOS depletion mode transistors. This circuit generates a square-wave with almost equal and fixed duty cycles. The second proposed circuit is able to control both on-duty and off-duty cycles independently with the help of two passive components, two NMOS depletion mode transistors, and two diodes connected to the circuit. The frequency of the proposed circuits can be adjusted with the passive components connected to the circuit. Moreover, electronic tuning can also be achieved with the proposed circuits. The measured results that are included in the paper show the linear variation of a time period as compared with existing OTRA based square waveform generator. The performance of the proposed circuits is examined while using SPICE models. These circuits are built on a laboratory breadboard using commercially available Current Feedback Operational Amplifier (AD844 AN) and passive components are connected externally and tested for square waveform generation. The obtained results demonstrate good agreement with the theoretical values. Full article

This paper presents the design and implementation of two front-ends for RF (Radio Frequency) energy harvesting, comparing them with the commercial one—P2110 by Powercast Co. (Pittsburgh, PA, USA) Both devices are implemented on a discrete element board with microstrip lines combined with lumped elements and are optimized for two different input power levels (−10 dBm and 10 dBm, respectively), at the GSM900 frequencies. The load has been fixed at 5kΩ, after a load-pull analysis on systems. The rectifiers stages implement two different Schottky diodes in two different topologies: a single diode and a 2-stage Dickson’s charge pump. The second one is compared with the P2110 by generating RF fields at 915 MHz with the Powercast Powerspot. The main aim of this work is to design simple and efficient low-cost devices, which can be used as a power supply for low-power autonomous sensors, with better performances than the current solutions of state-of-the-art equipment, providing an acceptable voltage level on the load. Measurements have been conducted for input power range −20 dBm up to 10 dBm; the best power conversion efficiency (PCE) is obtained with the second design, which reaches a value of 70% at 915 MHz. In particular, the proposed device exhibited better performance compared to the P2110 commercial device, allowing a maximum distance of operation of up to 22 meters from the dedicated RF power source, making it suitable even for IoT (Internet of Things) applications. Full article

Embedded Convolutional Neural Networks (ConvNets) are driving the evolution of ubiquitous systems that can sense and understand the environment autonomously. Due to their high complexity, aggressive compression is needed to meet the specifications of portable end-nodes. A variety of algorithmic optimizations are available today, from custom quantization and filter pruning to modular topology scaling, which enable fine-tuning of the hyperparameters and the right balance between quality, performance and resource usage. Nonetheless, the implementation of systems capable of sustaining continuous inference over a long period is still a primary source of concern since the limited thermal design power of general-purpose embedded CPUs prevents execution at maximum speed. Neglecting this aspect may result in substantial mismatches and the violation of the design constraints. The objective of this work was to assess topology scaling as a design knob to control the performance and the thermal stability of inference engines for image classification. To this aim, we built a characterization framework to inspect both the functional (accuracy) and non-functional (latency and temperature) metrics of two ConvNet models, MobileNet and MnasNet, ported onto a commercial low-power CPU, the ARM Cortex-A15. Our investigation reveals that different latency constraints can be met even under continuous inference, yet with a severe accuracy penalty forced by thermal constraints. Moreover, we empirically demonstrate that thermal behavior does not benefit from topology scaling as the on-chip temperature still reaches critical values affecting reliability and user satisfaction. Full article

Floating-point multipliers have been the key component of nearly all forms of modern computing systems. Most data-intensive applications, such as deep neural networks (DNNs), expend the majority of their resources and energy budget for floating-point multiplication. The error-resilient nature of these applications often suggests employing approximate computing to improve the energy-efficiency, performance, and area of floating-point multipliers. Prior work has shown that employing hardware-oriented approximation for computing the mantissa product may result in significant system energy reduction at the cost of an acceptable computational error. This article examines the design of an approximate comparator used for preforming mantissa products in the floating-point multipliers. First, we illustrate the use of exact comparators for enhancing power, area, and delay of floating-point multipliers. Then, we explore the design space of approximate comparators for designing efficient approximate comparator-enabled multipliers (AxCEM). Our simulation results indicate that the proposed architecture can achieve a 66% reduction in power dissipation, another 66% reduction in die-area, and a 71% decrease in delay. As compared with the state-of-the-art approximate floating-point multipliers, the accuracy loss in DNN applications due to the proposed AxCEM is less than 0.06%. Full article

The energy of real-time systems for embedded usage needs to be efficient without affecting the system’s ability to meet task deadlines. Dynamic body bias (BB) scaling is a promising approach to managing leakage energy and operational speed, especially for system-on-insulator devices. However, traditional energy models cannot deal with the overhead of adjusting the BB voltage; thus, the models are not accurate. This paper presents a more accurate model for calculating energy overhead using an analytical double exponential expression for dynamic BB scaling and an optimization method based on nonlinear programming with consideration of the real-chip parameter constraints. The use of the proposed model resulted in an energy reduction of about 32% at lower frequencies in comparison with the conventional model. Moreover, the energy overhead was reduced to approximately 14% of the total energy consumption. This methodology provides a framework and design guidelines for real-time systems and computer-aided design. Full article

Recently, the Logic-in-Memory (LiM) concept has been widely studied in the literature. This paradigm represents one of the most efficient ways to solve the limitations of a Von Neumann’s architecture: by placing simple logic circuits inside or near a memory element, it is possible to obtain a local computation without the need to fetch data from the main memory. Although this concept introduces a lot of advantages from a theoretical point of view, its implementation could introduce an increasing complexity overhead of the memory itself, leading to a more sophisticated design flow. As a case study, Binary Neural Networks (BNNs) have been chosen. BNNs binarize both weights and inputs, transforming multiply-and-accumulate into a simpler bitwise logical operation while maintaining high accuracy, making them well-suited for a LiM implementation. In this paper, we present two circuits implementing a BNN model in CMOS technology. The first one, called Out-Of-Memory (OOM) architecture, is implemented following a standard Von Neumann structure. The same architecture was redesigned to adapt the critical part of the algorithm for a modified memory, which is also capable of executing logic calculations. By comparing both OOM and LiM architectures we aim to evaluate if Logic-in-Memory paradigm is worth it. The results highlight that LiM architectures have a clear advantage over Von Neumann architectures, allowing a reduction in energy consumption while increasing the overall speed of the circuit. Full article

The wireless sensor nodes used in a growing number of remote sensing applications are deployed in inaccessible locations or are subjected to severe energy constraints. Audio-based sensing offers flexibility in node placement and is popular in low-power schemes. Thus, in this paper, a node architecture with low power consumption and in-the-field reconfigurability is evaluated in the context of an acoustic vehicle detection and classification (hereafter “AVDC”) scenario. The proposed architecture utilizes an always-on field-programmable analog array (FPAA) as a low-power event detector to selectively wake a microcontroller unit (MCU) when a significant event is detected. When awoken, the MCU verifies the vehicle class asserted by the FPAA and transmits the relevant information. The AVDC system is trained by solving a classification problem using a lexicographic, nonlinear programming algorithm. On a testing dataset comprising of data from ten cars, ten trucks, and 40 s of wind noise, the AVDC system has a detection accuracy of 100%, a classification accuracy of 95%, and no false alarms. The mean power draw of the FPAA is 43 $\mathsf{\mu }$ W and the mean power consumption of the MCU and radio during its validation and wireless transmission process is 40.9 mW. Overall, this paper demonstrates that the utilization of an FPAA-based signal preprocessor can greatly improve the flexibility and power consumption of wireless sensor nodes. Full article

A fully-integrated switched-capacitor (SC) DC-DC converter that steps down 2.0 V to 0.9 V with a peak efficiency of 80% is implemented in a 0.18 $\mathsf{\mu }$ m CMOS process. An ultra-low-power voltage-controlled oscillator that generates a wide range of switching frequencies is proposed to extend battery runtime. An efficiency >70% for load currents in the range of 12 $\mathsf{\mu }$ A to 17.8 mA is achieved by implementing a novel adaptively-biased pulse frequency modulation (ABPFM) technique in the controller. A symmetric charge-discharge topology with two-phase time interleaving is used as a power stage to reduce the output voltage ripple to <72 mV over the entire load current range. Full article

In this study, threshold voltage instability on commercial silicon carbide (SiC) power metal oxide semiconductor field electric transistor MOSFETs was evaluated using devices manufactured from two different manufacturers. The characterization process included PBTI (positive bias temperature instability) and pulsed IV measurements of devices to determine electrical parameters’ degradations. This work proposes an experimental procedure to characterize silicon carbide (SiC) power MOSFETs following two characterization methods: (1) Using the one spot drop down (OSDD) measurement technique to assess the threshold voltage explains temperature dependence when used on devices while they are subjected to high temperatures and different gate voltage stresses. (2) Measurement data processing to obtain hysteresis characteristics variation and the damage effect over threshold voltage. Finally, based on the results, it was concluded that trapping charge does not cause damage on commercial devices due to reduced value of recovery voltage, when a negative small voltage is applied over a long stress time. The motivation of this research was to estimate the impact and importance of the bias temperature instability for the application fields of SiC power n-MOSFETs. The importance of this study lies in the identification of the aforementioned behavior where SiC power n-MOSFETs work together with complementary MOS (CMOS) circuits. Full article

PH measurements are widely used in agriculture, biomedical engineering, the food industry, environmental studies, etc. Several healthcare and biomedical research studies have reported that all aqueous samples have their pH tested at some point in their lifecycle for evaluation of the diagnosis of diseases or susceptibility, wound healing, cellular internalization, etc. The ion-sensitive field effect transistor (ISFET) is capable of pH measurements. Such use of the ISFET has become popular, as it allows sensing, preprocessing, and computational circuitry to be encapsulated on a single chip, enabling miniaturization and portability. However, the extracted data from the sensor have been affected by the variation of the temperature. This paper presents a new integrated circuit that can enhance the immunity of ion-sensitive field effect transistors (ISFET) against the temperature. To achieve this purpose, the considered ISFET macro model is analyzed and validated with experimental data. Moreover, we investigate the temperature dependency on the voltage-current (I-V). Accordingly, an improved conditioning circuit is designed in order to reduce the temperature sensitivity on the measured pH values of the ISFET sensor. The numerical validation results show that the developed solution accurately compensates the temperature variation on the measured pH values at low power consumption. Full article

Due to the huge requirements in terms of both computational and memory capabilities, implementing energy-efficient and high-performance Convolutional Neural Networks (CNNs) by exploiting embedded systems still represents a major challenge for hardware designers. This paper presents the complete design of a heterogeneous embedded system realized by using a Field-Programmable Gate Array Systems-on-Chip (SoC) and suitable to accelerate the inference of Convolutional Neural Networks in power-constrained environments, such as those related to IoT applications. The proposed architecture is validated through its exploitation in large-scale CNNs on low-cost devices. The prototype realized on a Zynq XC7Z045 device achieves a power efficiency up to 135 Gops/W. When the VGG-16 model is inferred, a frame rate up to 11.8 fps is reached. Full article

Stochastic computing (SC) is an emerging low-cost computation paradigm for efficient approximation. It processes data in forms of probabilities and offers excellent progressive accuracy. Since SC’s accuracy heavily depends on the stochastic bitstream length, generating acceptable approximate results while minimizing the bitstream length is one of the major challenges in SC, as energy consumption tends to linearly increase with bitstream length. To address this issue, a novel energy-performance scalable approach based on quasi-stochastic number generators is proposed and validated in this work. Compared to conventional approaches, the proposed methodology utilizes a novel algorithm to estimate the computation time based on the accuracy. The proposed methodology is tested and verified on a stochastic edge detection circuit to showcase its viability. Results prove that the proposed approach offers a 12–60% reduction in execution time and a 12–78% decrease in the energy consumption relative to the conventional counterpart. This excellent scalability between energy and performance could be potentially beneficial to certain application domains such as image processing and machine learning, where power and time-efficient approximation is desired. Full article

Low power consumption has become one of the major requirements for most microelectronic devices and systems. Increasing power dissipation may lead to decreasing system efficiency and lifetime. The BULK metal oxide semiconductor field-effect transistor (MOSFET) has relatively high power dissipation and low frequency response due to its internal capacitances. Although the silicon-on-insulator (SOI) MOSFET was introduced to resolve these limitations, other challenges were introduced including the kink effect in the current-voltage characteristics. The selective buried oxide (SELBOX) MOSFET was then suggested to resolve the problem of the kink effect. The authors have previously investigated and reported the characteristics of the SELBOX structure in terms of kink effect, frequency, thermal and static power characteristics. In this paper, we continue our investigation by presenting the dynamic power characteristics of the SELBOX structure and compare that with the BULK and SOI structures. The simulated fabrication of the three devices was conducted using Silvaco TCAD tools in 90 nm complementary metal oxide semiconductor (CMOS) technology. Simulation results show that the average dynamic power dissipation of the CMOS BULK, SOI and SELBOX are compatible at high frequencies with approximately 54.5 µW. At low frequencies, the SOI and SELBOX showed comparable dynamic power dissipation but with lower values than the BULK structure. The difference in power dissipation between the SELBOX and BULK is in the order of nano watts. This power difference becomes significant at the chip level. For instance, at 1 MHz, SOI and SELBOX exhibit an average dynamic power consumption of 0.0026 µW less than that of the BULK structure. This value cannot be ignored when a chip operates using thousands or millions of SOI or SELBOX MOSFETs. Full article