Reconfigurable Support Vector Machine Classifier with Approximate Computing

Support Vector Machine (SVM) is one of the most popular machine learning algorithms. An energy-efficient SVM classifier is proposed in this paper, where approximate computing is utilized to reduce energy consumption and silicon area. A hardware architecture with reconfigurable kernels and overflow-resilient limiter is presented. For different applications, different kernels can be chosen and configured to achieve the optimum energy efficiency while achieving the performance requirement. For an epileptic seizure detection application, on average, 15% energy and 14% area savings are achieved with the proposed approximate SVM classifier compared to a fully-accurate SVM implementation with almost no accuracy degradation.