Spiking neural networks (SNNs) have gained huge attention as a potential energy-efficient
alternative to conventional artificial neural networks (ANNs) due to their inherent high …

Multi-pod systolic arrays are emerging as the architecture of choice in DNN inference
accelerators. Despite their potential, designing multi-pod systolic arrays to maximize …

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

Sparse training is one of the promising techniques to reduce the computational cost of deep
neural networks (DNNs) while retaining high accuracy. In particular, N: M fine-grained …

Stochastic Computing (SC) offers a promising computing paradigm for low-power and cost-
effective applications, with the added advantage of high error tolerance. In parallel, Coarse …

Once deployed on edge devices, a deep neural network model should dynamically adapt to
newly discovered environments and personalize its utility for each user. The system must be …

This article presents a mixed-computation neural network processing approach for edge
applications that incorporates low-precision (low-width) Posit and low-precision fixed point …

Depthwise convolution (DWConv) is an effective technique for reducing the size and
computational requirements of convolutional neural networks. However, DWConv's input …

Current systolic arrays still suffer from low performance and PE utilization on many real
workloads due to the mismatch between the fixed array topology and diverse DNN kernels …

Stochastic computing (SC) has emerged as a promising technique for reducing hardware
costs in various applications, particularly in multiply-accumulate (MAC) intensive tasks such …