Conventional multiply-accumulate (MAC) operations have long dominated computation time
for deep neural networks (DNNs), especially convolutional neural networks (CNNs) …

There is an increasing demand for next-generation flexible electronics in emerging low-cost
applications such as smart packaging and smart bandages, where conventional silicon …

From classical HPC to deep learning, MatMul is at the heart of today's computing. The recent
Maddness method approximates MatMul without the need for multiplication by using a hash …

In this paper, a table lookup-based computing technique is proposed to perform
convolutional neural network (CNN) inference without multiplication, and its FPGA …

The accuracy of neural networks has greatly improved across various domains over the past
years. Their ever-increasing complexity, however, leads to prohibitively high energy …

The emergence of neural network capabilities invariably leads to a significant surge in
computational demands due to expanding model sizes and increased computational …

DNN models are becoming increasingly large and complex, but they are also being
deployed on commodity devices that require low power and latency but lack specialized …