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 Nystrom method. 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.