Physical AI systems must perceive, reason, and act at once, under tight real-time latency and power budgets. That workload diversity breaks the one-processor model, widens the gap between trained models and deployable silicon code, and turns security from a point-in-time feature into a full-lifecycle requirement spanning safety-critical operation and multi-vendor supply chains. Engineering teams also face a hard architectural choice: a tightly coupled monolithic chip or a scalable, reusable multi-die design, each with its own latency, yield, and integration tradeoffs.
This eBook gives silicon and system architects a practical framework for solving these problems. It shows how to right-size heterogeneous inference to the application, how to anchor trust in hardware from boot through end of life, and how to weigh monolithic versus chiplet approaches against real workload, cost, and lifecycle constraints. The goal is a clear, lower-risk path from concept to production silicon, so teams reduce integration risk, control cost and schedule, and focus their effort on differentiation rather than rebuilding foundational infrastructure.
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