Overview
Artificial intelligence is transforming industries and society, but its high energy demands challenge global sustainability goals. Biological intelligence, in contrast, offers both good performance and exceptional energy efficiency. Neuromorphic computing, a growing field inspired by the structure and function of the brain, aims to create energy-efficient algorithms and hardware by integrating insights from biology, physics, computer science, and electrical engineering. This concise and accessible book delves into the principles, mechanisms, and properties of neuromorphic systems. It opens with a primer on biological intelligence, describing learning mechanisms in both simple and complex organisms, then turns to the application of these principles and mechanisms in the development of artificial synapses and neurons, circuits, and architectures. The text also delves into neuromorphic algorithm design, and the unique challenges faced by algorithmic researchers working in this area. The book concludes with a selection of practice problems, with solutions available to instructors online.
Full Product Details
Author: Shriram Ramanathan (Rutgers University, New Jersey) ,
Abhronil Sengupta (Pennsylvania State University)
Publisher: Cambridge University Press
Imprint: Cambridge University Press
Weight: 0.500kg
ISBN: 9781009564342
ISBN 10: 100956434
Pages: 350
Publication Date: 22 January 2026
Audience:
Professional and scholarly
,
Professional & Vocational
Format: Hardback
Publisher's Status: Forthcoming
Availability: Not yet available, will be POD
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon it's release. This is a print on demand item which is still yet to be released.
Author Information
Shriram Ramanathan is Rodkin-Weintraub Chair in Engineering at Rutgers University's College of Engineering. He previously held faculty positions at Purdue University and Harvard University, and was a research staff member at Components Research, Intel. Shriram's research focuses on adaptive semiconductors for neuromorphic computing and artificial intelligence, driving innovation at the intersection of materials science and next-generation AI technologies. He currently teaches the pioneering course 'Semiconductors for AI' at Rutgers. Abhronil Sengupta is Associate Professor in the School of Electrical Engineering and Computer Science at Penn State University, where he holds the prestigious Joseph R. and Janice M. Monkowski Career Development Professorship. As the director of the Neuromorphic Computing Lab, his research bridges hardware and software, focusing on sensors, devices, circuits, systems, and algorithms to enable low-power, event-driven cognitive intelligence. Abhronil also teaches the cutting-edge course 'Neuromorphic Computing' at Penn State.
