VLSI Researches for Machine Learning and Neuromorphic Computing

To accelerate performance per power in demanding machine learning applications in data centers, recently graphics processing units (GPU), field-programmable gate array (FPGA) and applicationspecific integrated circuit (ASIC) are broadly utilized by equipping massively parallel digital multiply-accumulators, where multiply-accumulation is an arithmetic bottleneck in software workloads using neural networks. Some of intelligent edge devices also become equipping ASIC or IP cores including such massively parallel multiply-accumulators optimized for specific machine learning applications at low-power consumption. This presentation introduces our current research activities in analog-based machine learning accelerators and analog-based spiking neural network processors aiming at high performance per power in machine learning applications by taking advantage of high-speed and low-power analog multiply-accumulation arithmetic over densely-packed synaptic non-volatile memory (NVM) resistive device arrays.