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This document describes a hardware architecture for efficiently evaluating elementary functions using a Radial Basis Function Network (RBFN). The proposed method uses a small lookup table (LUT) and a simple RBFN, which is a type of artificial neural network, to evaluate trigonometric, hyperbolic, exponential, logarithmic and square root functions. Compared to traditional function evaluation methods, the proposed method achieves over 2.5x power benefits and over 1.4x performance benefits while maintaining ac

Author

Ankita Arora

Language

EN