AXNet: ApproXimate computing using an end-to-end trainable neural network

The conflict between increasing demand for computing and sluggish grow of hardware capability triggers the heated development of approximate computing, which has achieved massive success in both industry and research community. Many applications that do not require utterly accurate computation can achieve tremendous acceleration and drastic reduction of the energy consumption by leveraging approximate computing, especially in domains that call for real-time calculation, fast response and low power consumption such as learning [27], image processing [19] and scientific computation [24]. Approximation computing can be conduct in different hierarchies, such as hardware [6], [18], system and software levels. Various approximate computing architectures [17], [19], [27] are advocated. Neural network (NN) based approximate computing focus on the acceleration in software-level and has many advantages when compared to previous methods. First, neural networks are proved to be able to fit any continuous function [12], and thus this method can universally be adopted by different tasks. Second, enormous parallelism in the neural networks is exploited by the rapid advancement of various neural network accelerators.