Low-precision computation is often used to lower the time and energy cost of machine
learning, and recently hardware accelerators have been developed to support it. Still, it has …

We present accumulator-aware quantization (A2Q), a novel weight quantization method
designed to train quantized neural networks (QNNs) to avoid overflow when using low …

Low-precision training reduces computational cost and produces efficient models. Recent
research in developing new low-precision training algorithms often relies on simulation to …

Hyperspectral image analysis has been gaining research attention thanks to the current
advances in sensor design which have made acquiring such imagery much more affordable …

Abstract Graph Neural Networks (GNNs) are emerging ML models to analyze graph-
structure data. Graph Neural Network (GNN) execution involves both compute-intensive and …

In the last years, Convolutional Neural networks (CNNs) found applications in many fields
from computer vision to speech recognition, showing outstanding results in terms of …

Graph Neural Networks (GNNs) are emerging models to analyze graph-structure data. GNN
execution involves both compute-intensive and memory-intensive kernels. The latter kernels …

Artificial intelligence experienced a technological breakthrough in science, industry, and
everyday life in the recent few decades. The advancements can be credited to the ever …

Deep neural networks achieve outstanding results for challenging image classification tasks.
However, the design of network topologies is a complex task, and the research community is …

The continued increase in memory, runtime and energy consumption of deployed machine
learning models on one side, and the trend to miniaturize intelligent devices and sensors on …