What Is Physical AI? Why It Demands a New Class of Silicon

Artificial intelligence is moving out of just the data center and into the physical world. Cars now steer themselves through traffic. Robots pick, sort, and assemble on factory floors and dance! Drones inspect infrastructure without a pilot. These machines share one defining trait: they run AI locally, in real time, to act on the world around them. This is physical AI, and it demands a fundamentally different kind of silicon than the chips that came before it.

This post opens a five-part series on designing silicon for the physical AI era. Here, we define what physical AI is, explain how it works, and show why traditional silicon falls short. We also introduce the four-pillar approach Cadence uses to help engineering teams accelerate from concept to production while reducing program risk.

What Is Physical AI?

Physical AI is the deployment of artificial intelligence directly onto edge devices, such as autonomous vehicles, robots, drones, and industrial machines, enabling them to perceive, reason, and act in the physical world in real time. Unlike cloud-based AI, physical AI runs local inference to deliver low latency, high reliability, deterministic performance, and continuous trust without depending on remote data centers.

Traditional edge products sense and respond. physical AI systems go further. They infer, adapt, protect data, and scale across product lines in real time. A physical AI-enabled robot does not simply detect an obstacle; it reasons about the obstacle, calculates a new path, and executes the movement within milliseconds. That leap in capability changes what the underlying silicon must do.

How Physical AI Differs from Cloud AI

Cloud AI processes data in centralized data centers with abundant power and compute. It tolerates network latency because many workloads are not time-critical. Physical AI operates under opposite constraints. Decisions happen on the device, often within a strict power, thermal, and size budget, and delays measured in milliseconds can compromise safety.

This difference drives the shift from cloud AI to physical AI. When a machine must act on its environment, sending sensor data to a remote server and waiting for a response is not viable. The intelligence has to live on the silicon inside the device.

The See, Reason, Act Framework

The most useful way to understand how physical AI works is the See, Reason, Act framework. It describes the three integrated stages every physical AI system moves through, continuously and in real time.

See (Perception)

Sensors collect raw environmental data. Cameras, LiDAR, radar, microphones, etc. capture the world around the device. This stage generates large, multimodal data streams that must be pre-processed efficiently before any reasoning can begin.

Reason (Understanding)

Multimodal and Vision-Language-Action (VLA) models process the sensor data to understand context. The system interprets physical constraints such as gravity and friction, weighs options, and makes safety-conscious decisions. This is the compute-intensive heart of physical AI, and it is where AI inference happens.

AI inference is the execution phase where a trained machine learning model processes new, real-world input to generate predictions, classifications, or decisions. In a robot, inference is the step where a camera feed is analyzed locally to identify obstacles and calculate immediate path adjustments.

Act (Interaction)

Actuators, motors, and grippers execute physical movements based on those decisions. The output of reasoning becomes motion in the real world. Because the loop runs continuously, any delay or error at this stage has immediate physical consequences.

Why Traditional Edge Silicon Falls Short

Traditional edge silicon was built for simpler workloads: reading a sensor, running a lightweight classifier or machine learning, and triggering a response. Physical AI overwhelms that model. Here is why the older approach cannot keep up.

• Multimodal workloads are heavier. Running vision, radar, and audio models together demands far more compute than a single-sensor task, along with the memory bandwidth to move data efficiently.
• Real-time constraints are stricter. Deterministic, low-latency inference is a safety requirement, not a nice-to-have. Traditional silicon rarely guarantees it.
• Power, performance, and area (PPA) budgets are tight. A drone or wearable cannot carry a data-center-class processor. The silicon must deliver high throughput per watt within severe SWaP (size, weight, and power) limits.
• Security was an afterthought. Older edge designs treated security as a software patch. Physical AI ties security directly to physical safety, so protection must be anchored in hardware.

The result is a clear need for a new class of edge silicon built on a heterogeneous compute stack, combining NPUs, DSPs, and CPUs that scale from always-on inference to complex, multimodal systems.

Why Physical AI Changes Security Requirements

In cloud AI, security focuses on data privacy and service uptime. In physical AI, security adds a further dimension of physical safety.

Because these systems control physical movement, a compromised sensor stream, an altered AI model, or a hijacked actuator command can cause immediate real-world harm. A tampered perception model in an autonomous vehicle is not a data breach; it is a collision risk. For this reason, physical AI security must be anchored in a hardware-based root of trust and maintained across the entire device lifecycle, from provisioning through interoperability to runtime.

Regulation is catching up. Frameworks such as the EU Cyber Resilience Act (CRA) are turning security from a consideration into a compliance deadline. Teams that build trusted execution into silicon from the start are the ones positioned to ship on time.

Key Verticals Driving Physical AI Silicon Demand

Physical AI is not a single market. It spans several verticals, each with distinct performance, safety, and integration requirements.

Automotive Autonomy

Vehicle autonomy requires right-sized AI inference, a unified software development platform, and trusted execution across the entire vehicle lifecycle. Automakers need to scale autonomy on a common AI architecture while protecting vehicle intelligence from manufacturing through operation and over-the-air updates.

Drones

Autonomous flight demands maximum airborne intelligence within the strictest SWaP constraints. Every milliwatt and gram counts, so silicon must deliver high inference throughput per watt while protecting flight systems and device integrity from boot through end of life.

Robotics

Robots must transform real-time perception into intelligent physical action across diverse platforms, from industrial arms to autonomous mobile robots. Silicon has to support increasingly complex, multimodal behavior while protecting connected models and data throughout deployment.

Aerospace and Defense

Aerospace and defense, which include the above applications plus further capabilities like secure communications, require mission-ready, evolvable AI platforms with secure, trusted execution across the full application lifecycle. These systems demand the highest assurance that onboard intelligence, data, and operations stay protected throughout their service life.

Figure 1: Physical AI applications

The Multi-Vendor Integration Problem

Turning physical AI ambition into silicon introduces a challenge that has little to do with AI itself: integration complexity.

Building a physical AI chip often means stitching together AI acceleration IP, security IP, memory and protocol interfaces, DSPs, and CPUs, frequently sourced from multiple vendors with different tools, workflows, and support models. Each handoff adds program risk. Interfaces may not align. Verification gaps emerge. Timelines slip. Multi-vendor stacks are a leading reason ambitious silicon programs stall or fail.

For CTOs and heads of engineering, this is the real bottleneck. The AI models are ready. The market demand is clear. The obstacle is the risk and cost of assembling a coherent, verified, secure silicon platform from disconnected parts.

The Path Forward: A Complete Path from Model to System

Physical AI needs a unified path that bridges the gap from AI model to trusted silicon to deployable system. Cadence structures this path around four pillars, each addressing one of the core challenges above. This series explores each pillar in depth.

1. Right-sized inference for physical AI: Scalable, silicon-proven NPU IP and a unified software flow let teams scale from always-on inference to hundreds of TOPS on a common architecture. One platform, one compiler, and multiple paths to integration replace a fragmented tool chain. (Covered in Blog 2.)

2. Trusted execution across the lifecycle: A hardware root of trust protects data, models, and devices from boot through end-of-life. Standards-based, post-quantum-ready, and EU CRA-ready security gets teams certification-ready before tape-out. (Covered in Blog 3.)

3. Scale further through standards-based modularity: Solutions for monolithic SoC designs or when they have reached their limits, a standards-based chiplet platform delivers cost efficiency and scalability from pre-verified, reusable building blocks. (Covered in Blog 4.)

4. One partner, from spec to silicon to system:  World-class IP, Electronic design automation (EDA), and custom silicon services close the gap from design to delivery, eliminating the integration risk that derails multi-vendor programs. (Covered in Blog 5.)

Together, these pillars form a complete approach: AI accelerated. Secure by design. Standards-based. Scalable.

Figure 2: Cadence physical AI silicon design pillars

Frequently Asked Questions

What is Physical AI in simple terms?

Physical AI is artificial intelligence that runs directly on physical machines, such as robots, vehicles, and drones, allowing them to perceive their surroundings, make decisions, and act in real time without relying on the cloud.

How is Physical AI different from generative AI?

Generative AI creates content such as text or images, usually in the cloud. Physical AI runs on edge devices and controls physical action in the real world, so it prioritizes low latency, deterministic performance, safety, and hardware-based security.

What is the See, Reason, Act framework?

See, Reason, Act is a three-stage framework describing how Physical AI works. The device perceives its environment through sensors (See), processes that data with local AI models to make decisions (Reason), and executes physical actions through actuators (Act).

Why can't traditional edge chips handle Physical AI?

Traditional edge silicon was designed for simple, single-sensor tasks. Physical AI demands heavier multimodal compute, stricter real-time performance, tight power and area budgets, and hardware-based security, which requires a new class of heterogeneous edge silicon combining NPUs, DSPs, and CPUs.

Why does Physical AI require hardware-based security?

Because physical AI systems control physical movement, a compromised model or sensor stream can cause real-world harm. Security must be anchored in a hardware root of trust and maintained across the full device lifecycle, rather than added later in software.

Key Takeaways

• Physical AI puts real-time AI inference on edge devices so they can perceive, reason, and act in the physical world.
• The See, Reason, Act framework captures how these systems operate continuously and under strict latency limits.
• Traditional edge silicon cannot meet Physical AI demands for multimodal compute, deterministic performance, tight PPA, and hardware-based security.
• Multi-vendor integration complexity is the leading source of program risk in Physical AI silicon design.
• A complete path from model to trusted silicon to system, built on four pillars, helps teams accelerate to production and reduce risk.

Next in the series: Right-Sized Inference for Physical AI, where we examine how to scale AI acceleration from always-on inference to complex multimodal systems on a common architecture.

To learn more, download the free ebook "Accelerating Silicon Design for Physical AI"

To explore how Cadence supports silicon design for the Physical AI era, connect with our team: contact@cadence.com