@techreport{oai:ipsj.ixsq.nii.ac.jp:00214042,
 author = {Qingyang, Yi and Heming, Sun and Masahiro, Fujita and Qingyang, Yi and Heming, Sun and Masahiro, Fujita},
 issue = {29},
 month = {Nov},
 note = {FPGA is appropriate for fix-point neural networks computing due to high power efficiency and configurability. However, its design must be intensively refined to achieve high performance using limited hardware resources. We present an FPGA-based neural networks accelerator and its optimization framework, which can achieve optimal efficiency for various CNN models and FPGA resources. Targeting high throughput, we adopt layer-wise pipeline architecture for higher DSP utilization. To get the optimal performance, a flexible algorithm to allocate balanced hardware resources to each layer is also proposed, supported by activation buffer design. Through our well-balanced implementation of four CNN models on ZC706, the DSP utilization and efficiency are over 90%. For VGG16 on ZC706, the proposed accelerator achieves the performance of 2.58x, 1.53x and 1.35x better than the referenced non-pipeline architecture [1], pipeline architecture [2] and [3], respectively., FPGA is appropriate for fix-point neural networks computing due to high power efficiency and configurability. However, its design must be intensively refined to achieve high performance using limited hardware resources. We present an FPGA-based neural networks accelerator and its optimization framework, which can achieve optimal efficiency for various CNN models and FPGA resources. Targeting high throughput, we adopt layer-wise pipeline architecture for higher DSP utilization. To get the optimal performance, a flexible algorithm to allocate balanced hardware resources to each layer is also proposed, supported by activation buffer design. Through our well-balanced implementation of four CNN models on ZC706, the DSP utilization and efficiency are over 90%. For VGG16 on ZC706, the proposed accelerator achieves the performance of 2.58x, 1.53x and 1.35x better than the referenced non-pipeline architecture [1], pipeline architecture [2] and [3], respectively.},
 title = {FPGA Based Accelerator for Neural Networks Computation with Flexible Pipelining},
 year = {2021}
}