Power dissipation limits and large margin in wireless sensors

Wireless smart sensors impose severe power constraints that call for power budget optimization at all levels in the design hierarchy. We elucidate a connection between statistical learning theory and rate distortion theory that allows to operate a wireless sensor array at fundamental limits of power dissipation. GiniSVM, a support vector machine kernel-based classifier based on quadratic entropy, is shown to encode the sensor data with maximum fidelity for a given constraint on transmission budget. The transmission power is minimized by GiniSVM in the form of a quadratic cost function under linear constraints. A classifier architecture that implements these principles is presented.