2022 IEEE Symposium on VLSI Technology and Circuits (VLSI Technology and Circuits) · 2022 · 12 citations · 0 references
EngineeringComputer EngineeringComputer ScienceLow-power DesignMachine LearningSystem-on-chip DesignAccelerator ArchitectureAccelerator IntegrationDeep LearningEnergy-efficient Embedded SystemsHardware AccelerationPower ManagementAcceleratorsDeep Learning AcceleratorsDynamic Power GatingImage Signal ProcessingLow-power Processor DesignPower-efficient Accelerator DesignSignal Processing AcceleratorsUltra-low-power Cmos
We present an ultra-low-power multimedia signal processor (MMSP) SoC that integrates a versatile deep neural network (DNN) engine with audio and image signal processing accelerators for cross-modal IoT intelligence. The proposed MMSP features 2MB MRAM to store all DNN weights on-chip with an energy-efficient dataflow using an MRAM-cache and dynamic power gating. The SoC achieves up to 3-10 TOPS/W peak energy efficiency and consumes only 0.25-3.84 mW. Being the first to demonstrate CNN, GAN, and back-propagation (BP) on a single accelerator SoC for cross-modal fusion, it outperforms state-of-the-art DNN processors by 1.4 - 4.5× in energy efficiency.