Multi-Precision Table-Addition Designs for Computing Nonlinear Functions in Deep Neural Networks

Computation of non-linear functions such as hyperbolic tangent and sigmoid is critical in machine learning. Furthermore, precision requirement varies across different layers in deep neural networks. Thus it is beneficial to design multi-precision function computation units. Since many function computation methods require lookup tables (LUT) along with simple arithmetic components, we propose a dual-precision architecture with shared LUT based on a recently proposed table-bound algorithm. Experimental results show that the proposed dual-precision designs has smaller area compared with the design with separate single-precision modules.