An Energy-Efficient Programmable Manycore Accelerator for Personalized Biomedical Applications

Abstract

Wearable personalized health monitoring systems can offer a cost-effective solution for human health care. These systems must constantly monitor patients’ physiological signals and provide highly accurate, and quick processing and delivery of the vast amount of data within a limited power and area footprint. These personalized biomedical applications require sampling and processing multiple streams of physiological signals with a varying number of channels and sampling rates. The processing typically consists of feature extraction, data fusion, and classification stages that require a large number of digital signal processing (DSP) and machine learning (ML) kernels. In response to these requirements, in this paper, a tiny, energy-efficient, and domain-specific manycore accelerator referred to as power-efficient nanoclusters (PENC) is proposed to map and execute the kernels of these applications. Simulation results show that the PENC is able to reduce energy consumption by up to 80% and 25% for DSP and ML kernels, respectively, when optimally parallelized. In addition, we fully implemented three compute-intensive personalized biomedical applications, namely, multichannel seizure detection, multiphysiological stress detection, and standalone tongue drive system (sTDS), to evaluate the proposed manycore performance relative to commodity embedded CPU, graphical processing unit (GPU), and field-programmable gate array (FPGA)-based implementations. For these three case studies, the energy consumption and the performance of the proposed PENC manycore, when acting as an accelerator along with an Intel Atom processor as a host, are compared with the existing commercial off-the-shelf general-purpose, customizable, and programmable embedded platforms, including Intel Atom, Xilinx Artix-7 FPGA, and NVIDIA TK1 advanced RISC machine -A15 and K1 GPU system on a chip. For these applications, the PENC manycore is able to significantly improve throughput and energy efficiency by up to $1872{\times}$ and $276{\times} $ , respectively. For the most computational intensive application of seizure detection, the PENC manycore is able to achieve a throughput of 15.22 giga-operations-per-second (GOPs), which is a $14{\times} $ improvement in throughput over custom FPGA solution. For stress detection, the PENC achieves a throughput of 21.36 GOPs and an energy efficiency of 4.23 GOP/J, which is $14.87{\times} $ and $2.28{\times} $ better over FPGA implementation, respectively. For the sTDS application, the PENC improves a throughput by $5.45{\times} $ and an energy efficiency by $2.37{\times} $ over FPGA implementation.