Hardware-based support vector machine classification in logarithmic number systems

Support vector machines are emerging as a powerful machine-learning tool. Logarithmic number systems (LNS) utilize the property of logarithmic compression for numerical operations. We present an implementation of a digital support vector machine (SVM) classifier using LNS in which, when compared with other implementations, considerable hardware savings are achieved with no significant loss in classification accuracy.

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