A 92 fps and 2.56 mJ/Frame Computing-In-Memory-Based Human Pose Estimation Accelerator With Resource-Efficient Macro for Mobile Devices

Abstract

Human pose estimation (HPE) is a promising solution for accurately understanding the state and context of human actions in virtual reality (VR). A high frame rate with low-power HPE processing is required for a realistic user interaction experience in battery-limited mobile devices. The proposed HPE accelerator is a computing-in-memory (CIM) based accelerator that computes depth-wise separable convolution (DWSC) of a lightweight HPE network. Three key features contribute to a resource-efficient CIM accelerator: 1) Dual-mode CIM computes DWSC with a reconfigurable homogenous architecture, resulting in 2.68 times higher throughput than previous analog CIMs. 2) Effective layeraware unrolling performs bit-parallel computation on dualmode CIM with fewer ADC operations, achieving 46 times higher throughput than before. 3) Adaptive fused inter-macro balancing improves latency balance in layer fusion execution, leading to a 57.0 % higher frame rate than before. The proposed HPE accelerator is implemented in 28nm CMOS technology. It achieves higher computation resource utilization and operates HPE with a low energy-delay product of 27.6 uJ7s in mobile VR devices.