On quality trade-off control for approximate computing using iterative training

Quality control plays a key role in approximate computing to save the energy and guarantee that the quality of the computation outcome satisfies users' requirement. Previous works proposed a hybrid architecture, composed of a classifier for error prediction and an approximate accelerator for approximate computing using well trained neural-networks. Only inputs predicted to meet the quality are executed by the accelerator. However, the design of this hybrid architecture, relying on one-pass training process, has not been fully explored. In this paper, we propose a novel optimization framework. It advocates an iteratively training process to coordinate the training of the classifier and the accelerator with a judicious selection of training data. It integrates a dynamic threshold tuning algorithm to maximize the invocation of the accelerator (i.e., energy-efficiency) under the quality requirement. At last, we propose an efficient algorithm to explore the topologies of the accelerator and the classifier comprehensively. Experimental results shows significant improvement on the quality and the energy-efficiency compared to the conventional one-pass training method.