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INDUSTRY

Robotics and physical AI

Real-time perception, sensor fusion, and on-device models for machines that move — not in the cloud.

THE CHALLENGE

What makes this hard

01

Deterministic latency

A robot can't wait for a cloud round-trip; perception has to keep pace with motion.

02

Every watt is runtime

On battery-powered platforms, silicon efficiency translates directly into operating hours.

03

Models are getting bigger

Physical AI is moving to transformers and vision-language-action models that older edge chips run poorly.

USE CASES

Where silicon makes the difference

01

Humanoids and legged robots

Multi-camera perception and on-device models for balance, navigation, and manipulation.

02

Autonomous mobile robots

Navigation, mapping, and obstacle detection in warehouses and factories.

03

Drones

Perception and inspection on board, where weight and power are the tightest.

04

Cobots and manipulation

Grasp planning at line speed, and human detection for safe motion alongside people.

WHERE PATHINAI FITS

Each stage, applied to robotics

Workload Intelligence

Captures the real sensor-to-actuator latency chain across cameras, IMU, and lidar.

Virtual Silicon

Models power draw under sustained real-time load, not burst benchmarks.

Architecture Explorer

Trades latency against power for your duty cycle.

Pre-Silicon Validation

Proves it on hardware before the robot does.

Manufacturing

Packaged, tested parts as our line comes online.

CLEAN FUTURE CF-1

Built for physical AI

CF-1 targets 20–32 TOPS at 3–5 W, with transformer support and multi-camera inputs — a co-processor that plugs in beside your robot's main compute board.

Explore CF-1

Talk to us about robotics

Tell us what your robotics product needs to run, and the budget it has to run on.

Talk to us

— From AI to Silicon

© [Year] PathinAI Technologies Private Limited