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(ANN), in particular, has made smart embedded systems an attractive option for a
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larger number of application areas. However, the high computational complexity,
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memory footprints, and energy requirements of machine learning models hinder their
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deployment on resource-constrained embedded systems. Most state-of-the-art works
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have considered this problem by proposing various low bit-width data representation …
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•Cites: Adaptive Posit: Parameter Aware Numerical Format for …
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[Adrian Freed, skywater-pdk] I added data on the newly-landed OSU cells to the table here http://diychip.org/sky130/ . For those who missed the FOSSI talk, they are doing high/medium/low for different track sizes. The 18T and 15T are there now. I have linked the documentation on the cells which includes rise and fall times and power consumption numbers. The Spice models for the cells have extensive parasitic extraction.