Primer to Neuromorphic Computing

Primer to Neuromorphic Computing by Harish Garg, Jyotir Moy Chatterjee, R Sujatha, and Shatrughan Modi is an accessible, modern introduction to the fast-evolving field of neuromorphic computing. Designed for students, researchers, and engineering professionals, this primer brings clarity to core concepts—spiking neural networks, event-driven architectures, device-level implementations (including memristive technologies), and low-power edge deployment—so you can bridge theory and practice with confidence.

Take a guided tour from fundamentals to application: clear explanations of neuron and synapse models, comparative analysis of analog, digital, and hybrid architectures, and practical design considerations for latency, scalability, and power efficiency. Real-world examples and worked problems illuminate how neuromorphic approaches accelerate AI on robotics, IoT, and sensor systems. The book also examines benchmarking strategies and emerging industry trends to help readers evaluate platforms and design choices.

Whether you are a graduate student in India or a systems engineer in a global R&D team, this primer equips you with the conceptual tools and problem-solving mindset needed to contribute to neuromorphic research and product development. Its concise, example-driven chapters make it ideal for classroom adoption, lab self-study, or a quick reference on the bookshelf.

If you’re ready to explore the next frontier of efficient AI hardware and design, Primer to Neuromorphic Computing is a practical, authoritative starting point. Order your copy today to start building neuromorphic solutions that work in the real world.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.