Design optimization for ADMM-Based SVM Training Processor for Edge Computing

This paper presents an optimized support vector machine (SVM) training processor employing the alternative direction method of multipliers (ADMM) optimizer. Low-rank approximation is exploited to reduce the dimension of the kernel matrix by employing the Nyström method. Verified in four datasets, the proposed ADMM-based training processor with rank approximation reduces 32× of matrix dimension with only 2% drop in inference accuracy. Compared to the conventional sequential minimal optimization (SMO) algorithm, the ADMM-based training algorithm is able to achieve a 9.8×10
⁷
shorter latency for training 2048 samples. Hardware optimization techniques, including pre-computation and memory sharing, are proposed to reduce the computational complexity by 62% and the memory usage by 60%. As a proof of concept, an epileptic seizure detector is designed to demonstrate the effectiveness of the proposed optimization techniques. The chip achieves a 153,310× higher energy efficiency and a 364× higher throughput-to-area ratio for SVM training than a high-end CPU. This work provides a promising solution for edge devices which require low-power and real-time training.