Physical intelligence – where embodied agents perceive, reason, plan, and act in the physical world – is emerging as a new computing frontier spanning robotics, autonomous systems, and spatial AI. However, today’s physical intelligence systems remain constrained by high latency, energy cost, and fragile reliability, due to fundamental mismatch between their compositional nature and existing computing architectures. The core challenge extends beyond algorithms, to how we architect computing systems and silicon that natively support intelligence that reasons and adapts under real-world constraints.

In this talk, I will present a principled cross-stack system-architecture-silicon co-design approach to building the computational foundations for physical intelligence. I will first introduce REASON, a flexible hardware architecture culminating the first programmable SoC tapeout for efficient neuro-symbolic cognition. REASON integrates unified kernel abstractions, flexible dataflows, memory-centric computing, end-to-end compilation flow, and adaptive power management, enabling efficient cognition in silicon. Building on this foundation, I will present ReCA, an integrated hardware architecture that bridges high-level cognition and low-level autonomy under stringent power and latency constraints by leveraging spatial-aware runtimes, memory layout optimizations, and heterogeneous fabrics. Finally, I will highlight our agile SoC design flows that translate evolving physical intelligence workloads into efficient silicon implementations.

By bridging computer architecture, system, and silicon validation, my research establishes adaptive, accelerator-rich computing substrates for physical intelligence. This work advances a vision in which AI and hardware are co-designed, co-reason, and co-adapt, architecting future computing systems as active enablers of intelligence in the physical world.