Physical AI differs from centralized AI infrastructure because it operates inside a continuous sensing, inference, and response cycle. These systems must act within bounded latency, power, thermal, reliability, and environmental limits, where delayed or inconsistent behavior can threaten equipment, processes, or human safety.
Edge AI is central to this architecture because many Physical AI systems cannot rely on remote infrastructure for immediate decisions. Sensor data may be too large, too frequent, too latency-sensitive, or too safety-critical to send away before acting. Local execution brings its own constraints, including limited power budgets, thermal variation, vibration, shock, humidity, and other physical operating conditions.
Meeting these constraints depends on silicon-level choices. Memory interfaces, interconnect, I/O subsystems, and die-to-die links carry, exchange, and synchronize data across the system, and variability in these resources directly affects response behavior. Physical AI systems also rarely operate alone; they participate in larger architectures spanning edge, industrial, vehicle, and cloud environments, so data movement and synchronization challenges extend well beyond a single device.