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The researchers developed their inference engine along side TinyNAS. TinyEngine generates the essential code necessary to run TinyNAS’ personalized neural network. Any deadweight code is discarded, which cuts down on compile-time. We keep only what we need,â€ says Han. And since we designed the neural network, we know exactly what we need. That’s the benefit of system-algorithm codesign.â€ In the group’s exams of TinyEngine, the size of the compiled binary code was between 1.9 and 5 occasions smaller than comparable microcontroller inference engines from Google and ARM. TinyEngine additionally incorporates innovations that scale back runtime, including in-place depth-wise convolution, which cuts peak reminiscence utilization almost in half. After codesigning TinyNAS and TinyEngine, Han’s team put MCUNet to the check.