Tensor processing units (TPUs), specialized hardware accelerators for machine learning
tasks, have shown significant performance improvements when executing convolutional …

As the Convolutional Neural Network (CNN) goes deeper and more complex, the network
becomes memory-intensive and computation-intensive. To address this issue, the …

The memristor crossbar, with its exceptionally high storage density and parallelism, enables
efficient vector matrix multiplication (VMM), significantly improving data throughput and …

This paper presents a study of methods for mapping the convolutional workloads in deep
neural networks (DNNs) onto the computing hardware in the in-memory computing (IMC) …

With the increased attention to memristive-based in-memory analog computing (IMAC)
architectures as an alternative for energy-hungry computer systems for machine learning …

In this work, we employ neural architecture search (NAS) to enhance the efficiency of
deploying diverse machine learning (ML) tasks on in-memory computing (IMC) …

This paper proposes PixelPrune, an approach to address two primary challenges in artificial
intelligence of things (AIoT) vision systems:(1) the energy-intensive analog-to-digital …

Given the growing focus on memristive crossbar-based in-memory computing (IMC)
architectures as a potential alternative to current energy-hungry machine learning hardware …

In recent years, spintronic devices and non-Von Neuman architectures have emerged as
two promising approaches to overcome the power, performance, and efficiency limitations of …

Abstract Machine learning (ML) has become ubiquitous, integrating into numerous real-life
applications. However, meeting the computational demands of ML systems is challenging …