Introduction to Neuromorphic Computing, 9781009564342
Hardcover
Unlock AI’s future: Brain-inspired computing for energy-efficient intelligence.
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Introduction to Neuromorphic Computing

$217.62

  • Hardcover

    350 pages

  • Release Date

    30 November 2025

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Summary

The Brain-Inspired Revolution: A Concise Introduction to Neuromorphic Computing

Artificial intelligence is revolutionizing industries, but its energy demands pose a threat to global sustainability. Biological intelligence, with its superior performance and energy efficiency, provides an alternative. Neuromorphic computing, an emerging field drawing inspiration from the brain’s structure and function, seeks to develop energy-efficient algorithms and hardware through the integration o…

Book Details

ISBN-13:9781009564342
ISBN-10:100956434X
Author:Shriram Ramanathan, Abhronil Sengupta
Publisher:Cambridge University Press
Imprint:Cambridge University Press
Format:Hardcover
Number of Pages:350
Release Date:30 November 2025
Weight:0g
About The Author

Shriram Ramanathan

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.

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