Fast, low power evaluation of elementary functions using radial basis function networks

Fast and efficient implementation of elementary functions such as sin(), cos(), and log() are of ample importance in a large class of applications. The state of the art methods for function evaluation involves either expensive calculations such as multiplications, large number of iterations, or large Lookup-Tables (LUTs). Higher number of iterations leads to higher latency whereas large LUTs contribute to delay, higher area requirement and higher power consumption owing to data fetching and leakage. We propose a hardware architecture for evaluating mathematical functions, consisting a small LUT and a simple Radial Basis Function Network (RBFN), a type of an Artificial Neural Network (ANN). Our proposed method evaluates trigonometric, hyperbolic, exponential, logarithmic, and square root functions. This technique finds utility in applications where the highest priority is on performance and power consumption. In contrast to traditional ANNs, our approach does not involve multiplication when determining the post synaptic states of the network. Owing to the simplicity of the approach, we were able to attain more than 2.5x power benefits and more than 1.4x performance benefits when compared with traditional approaches, under the same accuracy conditions.