TinyVers: A 0.8-17 TOPS/W, 1.7 Μw-20 Mw, Tiny Versatile System-on-chip with State-Retentive Emram for Machine Learning Inference at the Extreme Edge
2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits)（2022）
关键词
multiple ML models,energy efficiency,SoC,TinyVers,state-retentive eMRAM,machine learning inference,tiny versatile ultra-low power ML system-on-chip,enhanced intelligence,dataflow flexibility,multimodel support,on-chip power management,RISC-V host processor,block structured sparsity support,extreme edge smart sensing applications,deep sleep wake-up controller,aggressive on-chip power management,flexible ML accelerator,efficient zero-skipping,power 1.7 muW to 20.0 mW
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