Deep learning has become ubiquitous, touching daily lives across the globe. Today,
traditional computer architectures are stressed to their limits in efficiently executing the …

Computing systems are widely used in medical diagnosis, climate prediction, autonomous
vehicles, etc. As the key part of electronics, the performance of computing systems is crucial …

Transformers have achieved great success in a wide variety of natural language processing
(NLP) tasks due to the self-attention mechanism, which assigns an importance score for …

High-quality 3D image recognition is an important component of many vision and robotics
systems. However, the accurate processing of these images requires the use of compute …

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 …

Machine Learning (ML) inference is typically dominated by highly data-intensive Matrix
Vector Multiplication (MVM) computations that may be constrained by memory bottleneck …

Compute-in-Memory (CiM) hardware has shown great potential in accelerating Deep Neural
Networks (DNNs). However, most CiM accelerators for matrix vector multiplication rely on …

In-Memory Computing (IMC) has become a promising paradigm for accelerating machine
learning (ML) inference. While IMC architectures built on various memory technologies have …

The wide adoption of convolutional neural networks (CNNs) in many applications has given
rise to unrelenting computational demand and memory requirements. Computing-in-Memory …

Binary neural networks (BNNs) can transform multiply-and-accumulate (MAC) operations
into XNOR and accumulation (XAC), which has been proven to greatly reduce the hardware …