Memristive arrays are a natural fit to implement spiking neural network (SNN) acceleration.
Representing information as digital spiking events can improve noise margins and tolerance …

Stochastic rounding (SR) randomly maps a real number x to one of the two nearest values in
a finite precision number system. The probability of choosing either of these two numbers is …

The Posit™ Number System was introduced in 2017 as a replacement for floating-point
numbers. Since then, the community has explored its application in several areas, such as …

Training Deep Neural Networks (DNN) with high efficiency can be difficult to achieve with
native floating-point representations and commercially available hardware. Specialized …

The attention mechanisms of transformers effectively extract pertinent information from the
input sequence. However, the quadratic complexity of self-attention wrt the sequence length …

While low-precision optimization has been widely used to accelerate deep learning, low-
precision sampling remains largely unexplored. As a consequence, sampling is simply …

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 …

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

A continuing rise in DNN usage in distributed and embedded use cases has demanded
more efficient hardware execution in the field. Low-precision GeMMs with optimized data …

In recent times, a plethora of hardware accelerators have been put forth for graph learning
applications such as vertex classification and graph classification. However, previous works …