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In recent years, inexact computing has been increasingly regarded as one of the most promising approaches for reducing energy consumption in many applications that can tolerate a degree of inaccuracy. Driven by the principle of trading tolerable amounts of application accuracy in return for significant resource savings - the energy consumed, the (critical path) delay and the (silicon) area being the resources - this approach has been limited to certain application domains. In this paper, we propose to expand the application scope, error tolerance as well as the energy savings of inexact computing systems through neural network architectures. Such neural networks are fast emerging as popular candidate accelerators for future heterogeneous multi-core platforms, and have flexible error tolerance limits owing to their ability to be trained. Our results based on simulated 65nm technology designs demonstrate that the …