Power scalable implementation of Artificial Neural Networks

As the use of artificial neural network(ANN) in mobile embedded devices gets more pervasive, power consumption of ANN hardware is becoming a major limiting factor. Although considerable research efforts are now directed towards low-power implementations of ANN, the issue of dynamic power scalability of the implemented design has been largely overlooked. In this paper, we discuss the motivation and basic principles for implementing power scaling in ANN hardware. With the help of a simple example, we demonstrate how power scaling can be achieved with dynamic pruning techniques.