Journal of Low Power Electronics and Applications, Volume 12, Issue 4

Wireless Sensor Networks (WSNs) are considered to be among the most important scientific domains. Yet, the exploitation of WSNs suffers from the severe energy restrictions of their electronic components. For this reason there are numerous scientific...

In this paper, the most suited analog-to-digital (A/D) converters (ADCs) for Internet of Things (IoT) applications are compared in terms of complexity, dynamic performance, and energy efficiency. Among them, an innovative hybrid topology, a digital&n...

Computing data-intensive applications on the von Neumann architecture lead to significant performance and energy overheads. The concept of computation in memory (CiM) addresses the bottleneck of von Neumann machines by reducing the data movement in t...

A bootstrapping technique used to increase the intrinsic voltage gain of a bulk-driven MOS transistor is described in this paper. The proposed circuit incorporates a capacitor and a cutoff transistor to be connected to the gate terminal of a bulk-dri...

The edge computing paradigm for Internet-of-Things brings computing closer to data sources, such as environmental sensors and cameras, using connected smart devices. Over the last few years, research in this area has been both interesting and timely....

Self-sustainable energy harvesting for Internet of Things devices is challenging since ambient energy may be sporadic and unpredictable. This situation leads to frequent power failures that lead to intermittent operations, which prevent the reliabili...

We present a transfer learning method inspired by modulatory neurotransmitter mechanisms in biological brains and explore applications for neuromorphic hardware. In this method, the pre-trained weights of an artificial neural network are held constan...

Tunnel Field-Effect Transistors (TFETs) have been considered one of the most promising technologies to complement or replace CMOS for ultra-low-power applications, thanks to their subthreshold slope below the well-known limit of 60 mV/dec at room tem...

Convolutional Neural Networks (CNNs), due to their recent successes, have gained lots of attention in various vision-based applications. They have proven to produce incredible results, especially on big data, that require high processing demands. How...

Machine-learning accelerators rely on floating-point matrix and vector multiplication kernels. To reduce their cost, customized many-term fused architectures are preferred, which improve the latency, power, and area of the designs. In this work, we d...

In this study, a direct-grown helical-shaped tungsten-oxide-based (h-WO_x) selection device is presented for emerging memory applications. The selectivity in the selection devices is from 10 to 10³ with a low off-current of 0.1 to 0.01 nA. In addition...

Memristive neuromorphic systems represent one of the most promising technologies to overcome the current challenges faced by conventional computer systems. They have recently been proposed for a wide variety of applications, such as nonvolatile compu...

IoT embedded systems have multiple objectives that need to be maximized simultaneously. These objectives conflict with each other due to limited resources and tradeoffs that need to be made. This requires multi-objective optimization (MOO) and multip...

RISC-V is a modern Instruction Set Architecture (ISA) that, by its open nature in combination with a clean and modular design, has enormous potential to become a game changer in the Internet of Things (IoT) era. Recently, SystemC-based Virtual Protot...

Object detection is a predominant challenge in computer vision and image processing to detect instances of objects of various classes within an image or video. Recently, a new domain of vehicular platforms, e-scooters, has been widely used across dom...

In the new Internet of Things (IoT) era, embedded Field-Programmable Gate Array (FPGA) technology is enabling the deployment of custom-tailored embedded IoT solutions for handling different application requirements and workloads. Combined with the op...

This paper proposes a novel approach suitable to design energy-efficient approximate multipliers using both ASIC and FPGAs. The new strategy harnesses specific encoding logics based on bit significance and computes the approximate product performing...