BioCare: An Energy-Efficient CGRA for Bio-Signal Processing at the Edge

Abstract

Coarse Grained Reconfigurable Architectures (CGRAs) have proved to be viable platforms for health monitoring applications. Targeting energy-efficiency, state-of-the-art (SoA) CGRAs are augmented with approximation techniques, while still maintain acceptable accuracy at final Quality of Result (QoR). However, such CGRAs suffer from overheads of collecting separate Add/Mul/Div units. We propose BioCare as an area-and energy-efficient CGRA for health-monitoring edge devices, which exploits the synergistic effects of multiple approximations across HW/SW stack. BioCare offers different levels of energy-accuracy trade-off through the plasticity of its small PEs, each can support precision-adaptability with a Single Instruction, Multiple Data (SIMD) manner. BioCare demonstrates its superiority over SoAs, by achieving up to 32% and 67% area-and energy-savings, with 3.6× higher throughput. In addition to analysis on multiple kernels, evaluations on a multi-kernel ECG application shows that BioCare speed-ups the QRS detection latency by 61%, with 0% loss in accuracy. Our implementations will be available at

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“…Hence, state-of-the-art CGRA designs and CGRA generation frameworks provide methods to use custom data types and/or reduce the precision of the operations to increase efficiency. BioCare [10], a specialized design for bio-signal processing, integrates arithmetic-logic units (ALU) with two different execution modes (8-bit or 16-bit) that allow trading-off accuracy for energy efficiency. TRAN-SPIRE [32] leverages transprecision floating-point units [26] that can operate on several floating-point number formats in a single-instruction multiple data (SIMD) fashion to trade-off accuracy for performance.…”