DNN accelerators are often developed and evaluated in isolation without considering the
cross-stack, system-level effects in real-world environments. This makes it difficult to …

The use of deep learning (DL) to real-world applications, such as computer vision, speech
recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous …

Computing-in-memory (CIM) relieves the Von Neumann bottleneck by storing the weights of
neural networks in memory arrays. However, two challenges still exist, hindering the efficient …

This paper comparatively reviews sensing circuit designs for the most widely used
embedded memory, static random-access memory (SRAM). Many sensing circuits for SRAM …

In this paper, we propose to train binarized convolutional neural networks (CNNs) that are of
significant importance for deploying deep learning to mobile devices with limited power …

In this work, a ReRAM-based energy-efficient CIM accelerator is presented with two
techniques for edge AI applications. Firstly, a circuit-algorithm co-design scheme is …

SRAM-based in-memory Binary Neural Network (BNN) accelerators are garnering interests
as a platform for energy-efficient edge neural network computing thanks to their …

In-Memory Computing (IMC) has emerged as a promising paradigm for energy-efficient,
throughput-efficient and area-efficient machine learning at the edge. However, the …

In-memory computing using nonvolatile memory is a promising pathway to accelerate data-
intensive applications. While substantial research efforts have been dedicated to executing …

Analog Compute-in-Memory (CiM) accelerators are increasingly recognized for their
efficiency in accelerating Deep Neural Networks (DNN). However, their dependence on …