4.7 Article

ETA: An Efficient Training Accelerator for DNNs Based on Hardware-Algorithm Co-Optimization

Journal

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2022.3145850

Keywords

Training; Computational modeling; Data models; Quantization (signal); Field programmable gate arrays; Arrays; Throughput; Deep neural networks (DNNs); field-programmable gate array (FPGA); hardware accelerator; normalization; training

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This paper introduces a method for efficient training of deep neural networks on resource-constrained platforms. A hardware-algorithm co-optimization approach is used to implement an efficient training accelerator on an FPGA. The proposed training scheme reduces computational complexity and memory accesses while maintaining accuracy. Experimental results demonstrate state-of-the-art accuracy across multiple models.
Recently, the efficient training of deep neural networks (DNNs) on resource-constrained platforms has attracted increasing attention for protecting user privacy. However, it is still a severe challenge since the DNN training involves intensive computations and a large amount of data access. To deal with these issues, in this work, we implement an efficient training accelerator (ETA) on field-programmable gate array (FPGA) by adopting a hardware-algorithm co-optimization approach. A novel training scheme is proposed to effectively train DNNs using 8-bit precision with arbitrary batch sizes, in which a compact but powerful data format and a hardware-oriented normalization layer are introduced. Thus the computational complexity and memory accesses are significantly reduced. In the ETA, a reconfigurable processing element (PE) is designed to support various computational patterns during training while avoiding redundant calculations from nonunit-stride convolutional layers. With a flexible network-on-chip (NoC) and a hierarchical PE array, computational parallelism and data reuse can be fully exploited, and memory accesses are further reduced. In addition, a unified computing core is developed to execute auxiliary layers such as normalization and weight update (WU), which works in a time-multiplexed manner and consumes only a small amount of hardware resources. The experiments show that our training scheme achieves the state-of-the-art accuracy across multiple models, including CIFAR-VGG16, CIFAR-ResNet20, CIFAR-InceptionV3, ResNet18, and ResNet50. Evaluated on three networks (CIFAR-VGG16, CIFAR-ResNet20, and ResNet18), our ETA on Xilinx VC709 FPGA achieves 610.98, 658.64, and 811.24 GOPS in terms of throughput, respectively. Compared with the prior art, our design demonstrates a speedup of 3.65x and an energy efficiency improvement of 8.54x on CIFAR-ResNet20.

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