Over the past decades, emerging, data-driven machine learning (ML) paradigms have
increased in popularity, and revolutionized many application domains. To date, a substantial …

This article presents the design of a novel and compact spin-orbit torque (SOT)-based
ternary content addressable memory (TCAM). Experimentally validated/calibrated …

Ensuring the fairness of neural networks is crucial when applying deep learning techniques
to critical applications such as medical diagnosis and vital signal monitoring. However …

The pipeline is an efficient solution to boost performance in non-volatile memory based
computing in memory (nvCIM) convolution neural network (CNN) accelerators. However, the …

Rapid advancements in artificial intelligence have given rise to transformative models,
profoundly impacting our lives. These models demand massive volumes of data to operate …

Large Language Models (LLMs) deployed on edge devices learn through fine-tuning and
updating a certain portion of their parameters. Although such learning methods can be …

Multiple research vectors represent possible paths to improved energy and performance
metrics at the application-level. There are active efforts with respect to emerging logic …

In this work, we present a novel non-volatile spin transfer torque (STT) assisted spin-orbit
torque (SOT) based ternary content addressable memory (TCAM) with 5 transistors and 2 …

In this paper we present a comprehensive design and benchmarking study of Content
Addressable Memory (CAM) at the 7nm technology node in the context of similarity search …

Artificial intelligence is evolving with various algorithms such as deep neural network (DNN),
Transformer, recommendation system (RecSys) and graph convolutional network (GCN) …