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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• 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.

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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• 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.

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.

Select Guide Rating

• 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.