AI Computing Systems

Capture the future of intelligent infrastructure with AI Computing Systems, an authoritative guide by Yunji Chen, Ling Li, Wei Li, Qi Guo, Zidong Du, and Zichen Xu. This timely volume translates cutting-edge research into practical design strategies for engineers, researchers, and technical leaders shaping AI hardware and platforms worldwide.

Inside, discover clear explanations of AI system architecture, accelerator design, edge and cloud co‑deployment, energy-efficient inference, and system-level optimization techniques. Rich with real-world use cases, performance insights, and design trade-offs, the book balances theoretical rigor with hands-on applicability — ideal for professionals working in data centers, embedded systems, robotics, and telecommunications across North America, Europe, and the Asia-Pacific tech ecosystems.

What sets this work apart is its holistic approach: it connects algorithms to silicon, highlights scalability and reliability considerations, and provides actionable guidance for implementing neural network accelerators and optimized AI pipelines. Whether you’re refining next-generation chips, improving inference latency, or designing resilient AI services, you’ll gain practical frameworks and decision-making heuristics to accelerate development.

Clear, pragmatic, and forward-looking, AI Computing Systems is a must-have reference for anyone building AI infrastructure. Add it to your professional library to stay competitive in AI hardware, edge AI, and system optimization. Order now to bring best-practice insights from leading authors directly to your desk and advance your next-generation AI projects.

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