VLSI Implementation of LS-SVM Training and Classification using Entropy based Subset-Selection

Machine Learning techniques such as Support Vector Machines (SVM) have found applications in many fields, e.g. in Wireless Sensor Networks (WSN) and sensor data processing in general. Especially in the case of WSN energy is very limited as agents solely operate based on battery power after they have been deployed, therefore energy efficiency is of great importance. Furthermore, agents are supposed to adapt to their environment by being capable of re-training themselves based on feedback they get from their surroundings, which increases the computational demands on the digital hardware involved. To meet these demands, dedicated hardware in form of a very-large-scale integrated (VLSI) circuit is a reasonable approach and is investigated here. In this paper a specific variant of the SVM - the Least-Squares SVM - is implemented as VLSI circuit. Additionally during the training phase a subset-selection technique based on the quadratic Renyi entropy is implemented in order to reduce the computational and hardware demands. The resulting design consumes 21.35mW and requires an area of 81.2 kGE without memories.