General matrix multiplication (GEMM) is universal in various applications, such as signal
processing, machine learning, and computer vision. Conventional GEMM hardware …

Superconducting logic offers the potential to perform computation at tremendous speeds
and energy savings. However, a" semantic gap" lies between the level-driven logic that …

Unary computing, whose arithmetics require only one logic gate, has enabled efficient DNN
processing, especially on strictly power-constrained devices. However, unary computing still …

In this paper, we propose EdgeHD, a hierarchy-aware learning solution that performs online
training and inference in a highly distributed, cost-effective way. We use brain-inspired …

Structured, or tabular, data is the most common format in data science. While deep learning
models have proven formidable in learning from unstructured data such as images or …

Creating computing systems able to address our ever-increasing needs, especially as we
reach the end of CMOS transistor scaling, will require truly novel methods of computing …

Race logic is a relative timing code that represents information in a wavefront of digital
edges on a set of wires in order to accelerate dynamic programming and machine learning …

Domain-specific architectures have emerged as a promising solution to meet growing
technology demands but with this comes an urgent need to improve hardware …

Convolution is an important operation at the heart of many applications, including image
processing, object detection, and neural networks. While data movement and coordination …

Temporal Neural Networks (TNNs), inspired from the mammalian neocortex, exhibit energy-
efficient online sensory processing capabilities. Recent works have proposed a microar …