Neurons in a Spiking Neural Network (SNN) communicate using electrical pulses or spikes. They fire or trigger conditionally, and learning is sensitive to such triggers' timing and duration. The Leaky Integrate and Fire (LIF) model is the most widely used SNN neuron model. Most existing LIF-based neurons use a fixed spike frequency, which prevents them from attaining near-optimal accuracy. A research challenge is to design energy and area-efficient SNN neural cells that provide high learning accuracy and are scalable. Recently, the idea of tuning the spiking pulses in SNN was proposed and found promising. This work builds on the pulse-tuning idea by proposing an area and energy-efficient, stable, and reconfigurable SNN cell that generates spikes and reconfigures its pulse width to achieve near-optimal learning. It auto-adapts spike rate and duration to attain near-optimal accuracies for various SNN applications. The …

This paper presents a lightweight hybrid random number generator (HRNG), implemented and evaluated on a Field-Programmable Gate Array (FPGA). The proposed design enhances security and randomness by synergizing jitter and metastability using a feedforward topology, which achieves a near-perfect Shannon entropy. Moreover, it is validated using three distinct entropy metrics, guaranteeing statistically robust random numbers for security-sensitive applications. In addition to entropy evaluations, this design is also rigorously analyzed using multiple industry-standard randomness test suites. Beyond the FPGA implementation, this work presents performance metrics, including area utilization, power consumption, maximum frequency, and energy usage per random bit, which are synthesized across three different technology nodes in Synopsys Design Compiler (SDC). All of the results from the FPGA and the …

In recent years, the automotive industry has experienced a digital revolution, with vehicles increasingly equipped with computer systems, transitioning from purely mechanical machines to sophisticated autonomous entities. This evolution extends beyond individual vehicles to encompass Intelligent Transportation Systems (ITS), fundamentally altering transportation networks. While these advancements promise enhanced efficiency, safety, and travel experience, they also introduce new challenges, particularly in security and privacy. For example, AI-based systems are now used for real-time congestion prediction, allowing for optimized traffic flow; and predictive car maintenance, minimizing breakdowns and enhancing safety. However, these systems can be vulnerable to cyberattacks and data breaches. Integrating diverse technologies into transportation systems offers undeniable benefits, but it also has …

Hardware implementation of neural networks (NNs) is challenging due to varying application requirements. This often necessitates creating specific field programmable gate arrays (FPGAs) configurations from scratch for each application. This article proposes a flexible, self-supervised reconfigurable method to fit several application requirements by providing only the maximum available computational nodes a priori. The proposed method dynamically reconfigures the required number of hidden layers and nodes based on the application. The goal is to automatically determine the optimal NN configuration through reconfigurability to achieve maximum accuracy. Optimality is demonstrated through minimum average power, average delay, and area overhead, as well as maximum throughput and accuracy. Experimental results show that the proposed approach significantly reduces the optimized architecture search …

The Internet of Things (IoT) is a transformative technology facilitating seamless communication between diverse devices and systems, including resource-constrained devices. Speed efficiency and energy efficiency in communication protocols for IoT devices are crucial. The constrained application protocol (CoAP) is a promising, lightweight, and efficient protocol for IoT, offering robust messaging capabilities while conserving resources. An emerging research focus and challenge is designing hardware accelerators for CoAP that are fast, energy-efficient, and reliable. This article addresses that research challenge by proposing a CoAP hardware accelerator for optimizing message processing in resource-constrained IoT environments. The proposed accelerator’s architecture uses virtual channels (VCs) to manage incoming message traffic efficiently, enabling concurrent processing and enhancing throughput capacity.

Epilepsy triggers seizures, which develop before clinical onset in patients, and a timely and accurate prediction can save lives. A research challenge is to design accurate, fast, and energy-efficient hardware predictors. This work advances hardware-based seizure prediction research by proposing a new machine-learning-based predictor. It proposes a novel reconfigurable electroencephalogram (EEG) signal segmentation for increased learning. The proposed reconfigurable segmentation adaptively adjusts the overlap extent between consecutive segments and prepares new segments. Such prepared segments are fed into a Convolutional Auto-Encoder (CAE) using a proposed convolution module. The proposed convolution module uses optimized hyperparameters, including the number of layers, filters, filter size, pooling method, stride value, and padding for high learning and feature extraction. The learned CAE …