Physical AI is having a buzzword moment. From humanoid robots to autonomous vehicles and intelligent industrial machines, the idea of AI moving beyond screens and into the physical world is attracting significant attention. But beneath the hype lies a more fundamental question: what does it actually take to make AI work reliably when it has to sense, process, and respond to the world around it?
Traditional AI is largely concerned with understanding information and generating answers. Physical AI must go further: it must interact continuously with people, machines, and the surrounding environment.
From Edge AI to Physical AI
Physical AI is an evolution of Edge AI and embedded systems that introduces a crucial requirement: systems that perceive, decide, and act in real time. The shift from Edge AI to Physical AI actually signals a fundamental change. It is a shift not in what AI is, but in what AI does. As systems move from simply interpreting the world to actively interacting with it, the implications for semiconductor design are significant.
The addition of action means combining two different types of compute: intelligence and reasoning, and real-time execution. While the first is driven by AI models interpreting inputs, recognising patterns, making decisions, and determining what should happen next, the second is needed to make sure the system processes these signals, controls its interfaces, and carries out each decision at exactly the right time.
These workloads are complementary. AI provides the intelligence while deterministic compute ensures that intelligence can be applied reliably in the physical world.
This shift to Physical AI is part of an ongoing evolution of the Edge. While Industrial IoT was primarily about connectivity, gathering data from sensors for retrospective analysis, Edge AI moved the “thinking” closer to the source to save bandwidth and reduce latency. Physical AI builds on both of these but with a critical extra dimension: it closes the loop between perception and action. Whether it’s a robotic arm, an autonomous drone, or a haptic interface, Physical AI is adding execution to inference.
This difference is not just technical; it’s consequential.
From inference to action: why real-time matters
A 100ms lag in a chatbot is, at best, barely noticeable and, at worst, a minor inconvenience. A 100ms delay in a control loop could be a mechanical failure – with potentially catastrophic consequences.
This is why Physical AI isn’t a fundamental shift in algorithms, but a fundamental shift in system requirements. Traditional processor architectures are typically optimised for throughput, but physical AI operates in environments where timing is everything; where worst-case performance matters more than peak or average-case performance.
Robotics and AI marketing copy will celebrate low latency numbers as a measure of absolute speed and therefore quality. But in the real world, a system that responds in 10 milliseconds most of the time but occasionally in 20ms will feel and perform worse than one that always responds in 15ms. Systems must not just be fast, they must be reliably fast. This shifts the design priority toward determinism.
Deterministic execution ensures that tasks, whether they are neural network inferences or motor control adjustments, complete within known, predictable time bounds, regardless of the system load.
Thinking and acting = multi-tasking
As AI moves from the Cloud into products, the value of a system will increasingly depend on more than the capability of its AI model. Products must also meet strict requirements for responsiveness, power, cost, privacy, and reliability. This creates demand for a new execution layer: one that connects AI intelligence with deterministic real-world behaviour.
The need for these devices to perceive, decide, and act in real-time creates a system design need for parallelism. Physical AI workloads are inherently multi-modal and parallel, like a single device required to simultaneously manage multiple sensor inputs (such as audio, vision, or light detection), continuous data processing, real-time decision-making, and immediate actuation.
Architectures that rely on ‘best-effort’ time-slicing struggle with this balance. To meet the demands of Physical AI, silicon needs true hardware parallelism. This allows critical I/O and control tasks to run independently of the AI workload, ensuring that a spike in ‘thinking’ never starves the ‘doing’ of resources.
Physical AI is a decisive step forwards toward a world where machines don’t just process data, they are active participants in their environment. As we ask and expect more of our Edge systems, it is no longer sufficient to choose between thinking and acting, nor can we afford to choose between fast and reliable. Physical AI demands that the silicon we design must be able to handle parallel workstreams, and even more crucially, it must be on time every time.
