What happens when AI learns from the fire hose—and tests itself on silicon
What if your model pipeline started with a simple goal—your dataset, your target chip, and your latency or energy budget—and ended with measured results on real hardware? We sit down with Model Cat CEO Evan Petritis to explore how AI can build on-device AI through a closed loop that’s grounded in silicon, not estimates or hopeful benchmarks. From a live demo to a tour of their “chip farm,” we dig into how the platform searches architectures, tunes hyperparameters, and validates performance using vendor kernels and compilers across MCUs, MPUs, and specialized accelerators.
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