Towards Neuromorphic Machine Intelligence 1st Edition

Grab the future of low-power intelligent systems with Towards Neuromorphic Machine Intelligence, 1st Edition by Hong Qu. This authoritative guide pulls together cutting-edge neuromorphic computing concepts and practical insights, delivering a clear path from neuroscience-inspired principles to deployable machine intelligence.

Inside, you’ll find accessible explanations of spiking neural networks, synaptic plasticity, and event-driven architectures alongside discussions of neuromorphic hardware, algorithm-hardware co-design, and scalable learning rules. Hong Qu balances theory and application, illustrating how neuromorphic approaches reduce energy consumption and latency for edge AI tasks such as sensory processing, pattern recognition, and autonomous sensing. Carefully structured chapters make complex topics approachable for graduate students, researchers, and engineers seeking hands-on understanding of neuromorphic chips, architectures, and systems.

Why this book matters: neuromorphic machine intelligence is reshaping next-generation AI — enabling smarter devices in mobile, robotics, healthcare, and IoT. Whether you’re in academia, industry R&D, or an engineering team in North America, Europe, or Asia, this volume equips you with the frameworks and practical context needed to design efficient intelligent systems.

Compact yet comprehensive, Towards Neuromorphic Machine Intelligence is both a reference and a launchpad for innovation. Add it to your collection to advance your work in neuromorphic computing and stay ahead in the evolving landscape of energy-efficient AI. Order your copy today and start building the next wave of intelligent systems.

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