2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits) · 2022 · 29 citations · 0 references
EngineeringComputer EngineeringComputer ScienceLow-power DesignMachine LearningSystem-on-chip DesignEnergy-efficient Embedded SystemsHardware AccelerationLow-power Digital Circuit DesignPower ManagementPower OptimizationPower-aware DesignContinuous OperationLow-power Embedded SystemsLow-power MicroarchitectureLow-power System DesignRisc ArchitecturesRisc-v Core DesignSystem-on-chip Power ManagementTiny Machine Learning
This paper presents TinyVers, a tiny versatile ultra-low power ML system-on-chip (SoC) to bring enhanced intelligence to the Extreme Edge. TinyVers exploits dataflow flexibility for multi-model support, and aggressive on-chip power management optimized for Extreme Edge smart sensing applications. The SoC combines a RISC-V host processor, a 17 TOPS/W flexible ML accelerator with block structured sparsity support and efficient zero-skipping for deconvolution, a 1.7 μW deep sleep wake-up controller and an eMRAM for non-volatile storage, to perform up to 17.6 GOPS while achieving a power range from 1.7 μW-20 mW. Multiple ML models for diverse applications are mapped to show the flexibility and energy efficiency of the SoC with all models achieving 1-2 TOPS/W at less than 230 μW power for continuous operation.