INA: Incremental Network Approximation Algorithm for Limited Precision Deep Neural Networks

Abstract

Approximate computing is a promising paradigm to deal with large computing workloads in fault-tolerant applications, providing opportunities to improve hardware efficiency of Deep Neural Networks (DNNs). However, it is still difficult to apply highly approximate arithmetics (e.g., multipliers) to DNNs due to the effect of error accumulation and the convergence problem in re-training phase. To tackle this limitation, we propose a hardware-software co-design algorithm, namely Incremental Network Approximation (INA). By addressing the convergence problem, INA promotes fault tolerance of DNNs, and yields more tradeoffs between accuracy and implementation cost. Experiments show that the approximate inference models re-trained by INA could achieve up to 80% hardware reduction in various hardware design level, while the classification accuracy degradation is less than 2%. Moreover, the experiments also exhibit the generality of INA algorithm for applying to various approximate multiplier design.