Recently, due to the availability of big data and the rapid growth of computing power,
artificial intelligence (AI) has regained tremendous attention and investment. Machine …

In recent years, the limits of the multicore approach emerged in the so-called “dark silicon”
issue and diminishing returns of an ever-increasing core count. Hardware manufacturers …

Deep Neural Networks (DNNs) have achieved extraordinary performance in various
application domains. To support diverse DNN models, efficient implementations of DNN …

Large deep neural network (DNN) models pose the key challenge to energy efficiency due
to the significantly higher energy consumption of off-chip DRAM accesses than arithmetic or …

As its core computation, a self-attention mechanism gauges pairwise correlations across the
entire input sequence. Despite favorable performance, calculating pairwise correlations is …

Processing-in-memory (PIM) is a promising architecture to design various types of neural
network accelerators as it ensures the efficiency of computation together with Resistive …

Processing-In-Memory (PIM) is an emerging technology that addresses the memory
bottleneck of graph processing. In general, analog memristor-based PIM promises high …

This paper first presents an input-stationary (IS) implemented crossbar accelerator (INCA),
supporting inference and training for deep neural networks (DNNs). Processing-in-memory …

Neural network (NN) accelerators have been integrated into a wide-spectrum of computer
systems to accommodate the rapidly growing demands for artificial intelligence (AI) and …

ReRAM-based Processing-In-Memory (PIM) architectures have been increasingly explored
to accelerate various Deep Neural Network (DNN) applications because they can achieve …