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Network Compression for end-to-end trainable neural networks using Approximate Computing

Approximate computing is increasing in demand to reduce the resources and time needed to run applications for several error-resilient applications. Neural networks through approximate computation is a popular framework able to achieve significant energy consumption. The proposed research aims to improve the quality of the approximation and energy efficiency gain. Two neural networks that are an approximator and a predictor are implemented in this research work to obtain a good approximation quality. The approximator generates the approximation results, although the predictor determines input data is reliable to approximation with the provided prerequisites for quality or not. Neural networks do incredible things, but they suffer from computing and memory obstacles that limit their use. This research also aims to create a method that can compress neural networks to minimize memory and reduce computing capacity with negligible loss of accuracy. Experimental results show remarkable improvements in approximation quality and energy-efficiency compared to the “OnePass” training and “Iterative” training. The experimental results show a 50% more invocation and reduction in training time when compared with the previous approximated neural network frameworks.