SLIT: An Energy-Efficient Reconfigurable Hardware Architecture for Deep Convolutional Neural Networks

Abstract

Convolutional neural networks (CNNs) have dominated a range of applications, from advanced manufacturing to autonomous cars. For energy cost-efficiency, developing low-power hardware for CNNs is a research trend. Due to the large input size, the first few convolutional layers generally consume most latency and hardware resources on hardware design. To address these challenges, this paper proposes an innovative architecture named SLIT to extract feature maps and reconstruct the first few layers on CNNs. In this reconstruction approach, total multiplyaccumulate operations are eliminated on the first layers. We evaluate new topology with MNIST, CIFAR, SVHN, and ImageNet datasets on image classification application. Latency and hardware resources of the inference step are evaluated on the chip ZC7Z020-1CLG484C FPGA with Lenet-5 and VGG schemes. On the Lenet-5 scheme, our architecture reduces 39% of latency and 70% of hardware resources with a 0.456 W power consumption compared to previous works. Even though the VGG models perform with a 10% reduction in hardware resources and latency, we hope our overall results will potentially give a new impetus for future studies to reach a higher optimization on hardware design. Notably, the SLIT architecture efficiently merges with most popular CNNs at a slightly sacrificing accuracy of a factor of 0.27% on MNIST, ranging from 0.5% to 1.5% on CIFAR, approximately 2.2% on ImageNet, and remaining the same on SVHN databases. key words: primary visual cortex, image classification, convolutional neural network, hardware architecture, FPGA, feature extraction