Deep neural networks (DNNs) are being incorporated in resource-constrained IoT devices,
which typically rely on reduced memory footprint and low-performance processors. While …

The occurrence of radiation-induced soft errors in electronic computing systems can either
affect non-essential system functionalities or violate safety–critical conditions, which might …

Driven by the success of machine learning algorithms for recognizing and identifying
objects, there are significant efforts to exploit convolutional neural networks (CNNs) in edge …

Deep neural network (DNN) models are being deployed in safety-critical embedded devices
for object identification, recognition, and even trajectory prediction. Optimized versions of …

This article compares and assesses the effectiveness of three prominent machine learning
(ML) models for tiny ML computing systems in tolerating neutron-induced soft errors. Results …

The adoption of RISC-V processors bloomed in recent years, mainly due to its open
standard and free instruction set architecture. However, much remains to help software …

Large Language Models (LLMs) have catalyzed transformative advances across a spectrum
of natural language processing tasks through few-shot or zero-shot prompting, bypassing …

Security and reliability problems in edge devices can become the Achilles' heel for their
massive use in Internet of Things (IoT) systems. While most works address security by …

Integrating Machine Learning (ML) inference models into edge computing devices has
introduced several challenges related to improving power efficiency, performance, and …

Incorporating Machine Learning (ML) inference models into edge computing devices has
presented some performance and reliability enhancement challenges. Multi-threaded ML …