In this article, we investigate the reliability of Google's coral tensor processing units (TPUs)
to both high-energy atmospheric neutrons (at ChipIR) and thermal neutrons from a pulsed …

We empirically evaluate an undervolting technique, ie, underscaling the circuit supply
voltage below the nominal level, to improve the power-efficiency of Convolutional Neural …

Convolutional Neural Networks (CNNs) have become the most used and efficient way to
identify and classify objects in a scene. CNNs are today fundamental not only for …

Dedicated hardware is required to efficiently execute the highly resource-demanding
modern artificial neural networks (ANNs). The high complexity of ANN systems has …

EdgeAI is an emerging artificial intelligence (AI) accelerator technology, which is capable of
delivering improved AI performance at both a lower cost and a lower power level. With the …

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 …

This abstract presents the result of extensive reliability evaluation of Google's Coral Tensor
Processing Unit (TPU), which is one of the latest low power accelerators for CNNs. We …

Inspired by the human brain's structure and function, neuromorphic computing has emerged
as a promising approach for developing energy-efficient and powerful computing systems …

The present article applies the-nearest neighbors (-NN) machine learning (ML) algorithm to
detect and correct single-event upsets (SEUs). In particular, this work focuses on SEUs …