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INDUSTRY

Automotive

ADAS and in-cabin AI within tight power, thermal, and safety budgets.

THE CHALLENGE

What makes this hard

01

Power and thermal limits

Sealed modules with little or no airflow leave no room for silicon that wastes watts.

02

Functional safety

Safety requirements such as ISO 26262 shape the architecture from the start — a failure mode has real consequences.

03

Long lifecycles

Vehicles stay on the road for a decade or more, so parts must be supported and qualified for the long haul.

USE CASES

Where silicon makes the difference

01

Driver monitoring

Drowsiness and distraction detection that runs continuously inside the cabin.

02

Surround-view and parking

Multi-camera perception for low-speed manoeuvres.

03

In-cabin sensing

Occupant detection, gesture, and voice processed on the device.

04

Sensor fusion

Combining camera, radar, and other inputs for driver assistance.

WHERE PATHINAI FITS

Each stage, applied to automotive

Workload Intelligence

Profiles real driving-scenario inference loads instead of generic benchmarks.

Virtual Silicon

Models power and thermal behaviour under sustained load, not short bursts.

Architecture Explorer

Weighs safety-margin trade-offs explicitly alongside performance.

Pre-Silicon Validation

Proves the design on FPGA with real sensor data before tapeout.

Manufacturing

A qualified path to packaged, tested units as our line comes online.

CLEAN FUTURE CF-1

Low-power inference for the cabin and beyond

CF-1 is designed for in-cabin and perception workloads where power and heat are tightly budgeted.

Explore CF-1

Talk to us about automotive

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

Talk to us

— From AI to Silicon

© [Year] PathinAI Technologies Private Limited