Autorentext
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.

Klappentext
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.

Inhalt

Preface; 1. Intelligence in Biological Systems; 2. Principles of Artificial Neural Networks; 3. Artificial Synapses; 4. Artificial Neurons; 5. Examples of Applications in Artificial Neural Networks; 6. System Design; 7. Neuromorphic Algorithms; 8. Lifelong Learning with AI Algorithms and Hardware; 9. Practice Problems.