ApproxCNN: Evaluation Of CNN With Approximated Layers Using In-Exact Multipliers

Approximate computing in the hardware design space has gained attention owing to the significant benefits achieved in power savings, performance improvement, and compact spacing. Most of the arithmetic operations including addition, multiplication, division, and even a few of the activation functions are realized using approximate computing techniques in the past. These are primarily applicable for error-resilient image processing applications where the visual perception of humans has limited abilities to distinguish between the original and approximated image. This has been extended to other signal-processing domains under similar constraints on human sensing modalities. Although most of the work intends to apply approximate computing on Artificial Intelligence (AI) workloads, but realizing approximate computing on the same has not been feasible. Hence, this has deprived to feel the impact of hardware benefits in conjunction with network accuracy compromises. This research work aims to establish the adoption of a specific set of approximate multipliers in the Convolutional Neural Network (CNN) and present hardware characteristics along with the validation accuracy of the network. Eight different approximate multipliers that are categorized along the positive and negative error distributions are applied to 6-layer, 3-layer, and 1-layer CNNs that are trained on benchmark datasets of CIFAR-10, MNIST, and F-MNIST respectively. The extensive design space exploration has allowed extracting the optimal sequence of approximate multipliers along different layers of CNNs reporting hardware gain and least accuracy drop. For the 6-layered CNN, the optimal hardware design with approximation applied for 1
^st
, and 3
^rd
layer offered hardware gains of 16.5%, 10.2%, 2.4%, and 18.2% in power, footprint, delay, and PDP respectively, with comparable validation accuracy as that of exact CNN. Similarly, the best 3 layered approximated CNN model offered an improvement of 38.09%, 31.57%, 5.97%, and 43.84% in power, area, delay, and PDP parameters respectively over exact CNN model, without much drop in the validation accuracy. The 3 layered CNN demonstrated alternate arrangement of positive and negative error distributed type of multipliers along the layers to minimize the errors and maintain model accuracy close to the original exact multiplier adopted model. The correlation between five different error metrics for the sequence of approximate multipliers applied along the layers of CNNs and the overall accuracy loss in network inference were computed. The proposed work sets an example to leverage any approximate multipliers along the layers of CNN in the future and estimate the most optimal hardware design alongside retain the original network accuracy. The work is a step towards designing custom power-performance-area efficient neural network accelerators.