AxR-NN: Approximate Computation Reuse for Energy-Efficient Convolutional Neural Networks

The recent success of convolutional neural networks (CNN) has led its implementation in specialized accelerators such as graphics processing unit (GPUs). However, the intensive computing workloads of CNNs remain a challenge to existing accelerators. By leveraging the error tolerance of CNNs, we propose a novel method to design energy-efficient CNN accelerators using approximate computation reuse (ACR), referred to as AxRNN. Computation reuse aims to reuse the previously computed results to avoid redundant executions. However, it cannot be applied directly to CNNs because CNNs do not have enough data locality. Thus, AxRNN performs approximate computation reuse under relaxed precision requirements on input patterns and design a reconfigurable architecture to support the ACR. This reconfigurable pattern matching is central to achieve a "controllable approximation". We implement the AxRNN using content addressable memory and integrate them with floating point units. Simulation results show that AxRNN reduces the computation energy by 30-58% with only 1-2.5% accuracy degradation on MNIST, EMNIST, and CIFAR-10 dataset.