Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted
in breakthroughs in many areas. However, deploying these highly accurate models for data …

In the modern-day era of technology, a paradigm shift has been witnessed in the areas
involving applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep …

The advent of Deep Learning (DL) has radically transformed the computing industry across
the entire spectrum from algorithms to circuits. As myriad application domains embrace DL, it …

Package-level integration using multi-chip-modules (MCMs) is a promising approach for
building large-scale systems. Compared to a large monolithic die, an MCM combines many …

This paper presents Timeloop, an infrastructure for evaluating and exploring the architecture
design space of deep neural network (DNN) accelerators. Timeloop uses a concise and …

Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

Memristor crossbars are circuits capable of performing analog matrix-vector multiplications,
overcoming the fundamental energy efficiency limitations of digital logic. They have been …

In recent years, convolutional neural networks (CNNs) have shown great performance in
various fields such as image classification, pattern recognition, and multi-media …

This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …

In recent years, it has been witnessed that the systolic array is a successful architecture for
DNN hardware accelerators. However, the design of systolic arrays also encountered many …