Physical AI demands more than traditional edge silicon. Devices must perceive, reason, and act in real time while protecting data and scaling across product lines. This eBook shows how to move from concept to production while reducing the risk and complexity of a multi-vendor stack.

What You Will Learn:

Realizing Physical AI spans architecture, IP selection, software enablement, verification, and high-yield silicon. Explore the Cadence approach across four pillars that take you from AI model to deployable system:

• Right-Sized Inference for Physical AI. Scale from always-on, ultra-low-power inference to complex, multimodal systems with the Neo NPU and Neo MX subsystem, using a single SDK compiler flow across NPUs, DSPs, and CPUs.
• Trusted Execution Across the Lifecycle. Protect data, models, and devices from boot through end-of-life with a hardware root of trust and certification-ready, EU CRA-ready lifecycle management for monolithic and chiplet-based systems.
• Scale Further Through Standards-Based Modularity. Choose monolithic or chiplet integration without rearchitecting the core design. The pre-verified Physical AI Chiplet Platform enables scalable, cost-efficient product diversification using reusable, interoperable building blocks aligned with OCP FCSA and UCIe.
• One Partner, From Spec to Silicon to System. Combine world-class IP, including memory, protocol, vision, and audio solutions, with EDA flows and custom silicon services to reduce integration risk across the entire program.
• A Decision Framework to De-Risk Your Roadmap. Apply a practical framework for right-sizing inference, establishing hardware-based trust, and choosing monolithic versus chiplet architectures. The result is a Physical AI roadmap that starts with a path you can commit to.

Read more here.

Fig.1: Physical AI applications. Source: Cadence

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