Abstract
Convolutional neural networks (CNNs) have been widely applied for image recognition, face detection, and video analysis because of their ability to achieve accuracy close to or even better than human level perception. However, different features of convolution layers and fully connected layers have brought many challenges to the implementation of CNN on FPGA platforms, because different accelerator units must be designed to process the whole networks. In order to overcome this problem, this work proposes a pipelined accelerator towards uniformed computing for convolutional neural networks. For the convolution layer, the accelerator first repositions the input features into matrix on-the-fly when they are stored to FPGA on-chip buffers, thus the computation of convolution layer can be completed through matrix multiplication. For the fully connected layer, the batch-based method is used to reduce the required memory bandwidth, which also can be completed through matrix multiplication. Then a pipelined computation method for matrix multiplication is proposed to increase the throughput and also reduce the buffer requirement. The experiment results show that the proposed accelerator surpasses CPUs and GPUs platform in terms of energy efficiency. The proposed accelerator can achieve the throughput of 49.31 GFLOPS, which is done using only 198 DSP modules. Compared to the state-of-the-art implementatuion, our accelerator has better hardware utilization efficiency.
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Acknowledgements
This work was supported by the NSFC (No. 61379040), Anhui Provincial NSF (No. 1608085QF12), Suzhou Research Foundation (No. SYG201625), CCF-Venustech Hongyan Research Initiative (No. CCF-VenustechRP1026002), Youth Innovation Promotion Association CAS (No. 2017497), and Fundamental Research Funds for the Central Universities (WK2150110003).
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Sun, F., Wang, C., Gong, L. et al. UniCNN: A Pipelined Accelerator Towards Uniformed Computing for CNNs. Int J Parallel Prog 46, 776–787 (2018). https://doi.org/10.1007/s10766-017-0522-1
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DOI: https://doi.org/10.1007/s10766-017-0522-1