Hardware-Aware AI, Not Just Bigger Models
What if the obstacle to fast, reliable AI isn’t your dataset or your optimizer—but the silicon under your model? We dig into why performance collapses when architecture and hardware don’t align, and we lay out a clear path to ship models that actually fly on the devices your users own. Starting with the Ferrari-and-hummingbird metaphor, we show how theoretical efficiency—FLOPs, parameters, even TOPS—often fails to predict real-world latency, power, and user experience.
We walk through a surprising benchmark: MobileNet V2, small and “efficient,” runs slower than an older ResNet18 on GPUs…
The skinny
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