An Exploration Framework for Efficient High-Level Synthesis of Support Vector Machines: Case Study on ECG Arrhythmia Detection for Xilinx Zynq SoC

In recent years, Support Vector Machine (SVM) classifiers have played a crucial role in providing data fusion and high accuracy classification solutions for various, complex, non-linear problems. Their popularity accompanied by the ever-increasing need of implementing it on computationally weak, portable or even wearable systems has refueled the effort to accelerate their execution. In this paper, we explore FPGA-based acceleration to produce efficient SVM hardware co-processors. We propose a systematic two-level approach for SVM acceleration, which first optimizes the global structure of the original SVM's behavioral description to exploit the data- and instruction-level parallelism and then further refines it through a targeted design exploration that matches the accelerator's memory architecture to its computation and memory access patterns. The proposed methodology has been implemented as a framework on top of Vivado High-Level Synthesis (HLS) tool. We evaluate the effectiveness of the methodology through a rich set of analysis and validation results, which show that its adoption delivers SVM accelerator designs achieving latency gains of up to 98.78 % in respect to Vivado-HLS default optimized solution. Finally, using as a case study an ECG analysis and Arrhythmia detection system we show that a target Zynq programmable SoC utilizing the optimized SVM accelerator design outperforms pure software implementations in numerous single or dual core target platforms, achieving speedups, which range from 10x up to 78x.