A RRAM-based High Energy-efficient Accelerator Supporting Multimodal Tasks for Virtual Reality Wearable Devices
Xin Zhao, Zhiming Hu, Zilong Guo, Haodong Fan, Xi Yang, Jing Zhou, Liang Juan Chang · 2024
Virtual reality (VR) wearable devices can achieve immersive entertainment by fusing multi-modal tasks from various senses. However, constrained by the short battery life and limited hardware resources of the VR devices, running multiple tasks simultaneously with different modals is difficult. In this paper, we propose an energy-efficient accelerator that supports Multi-modal Tasks for VR devices, namely MTVR. We present a multi-task computing solution based on the flexible multi-task computing core design and efficient computing unit allocation strategy, which simultaneously achieves efficient work of multi-modal tasks. We design an early exit detector to skip invalid calculations, greatly saving energy. In addition, a fine-grained tiny value skip method at multiplier and adder levels is proposed to save energy further. We provide a hybrid RRAM and SRAM memory access scheme, reducing the external memory access (EMA). Through experimental evaluation, the multitask computing core achieves an average computational utilization of 95%. When the invalid input ratio is 90%, energy saving brought by the early exit detector can reach 88%. The tiny value skip method further achieved 13% energy saving. Hybrid memory access scheme obtains 98.9% EMA reduction. We deployed the MTVR accelerator in FPGA and self-designed RRAM, achieving energy efficiency of 3.6 TOPS/W, higher than other single-task accelerators.