Knowledge Commons of Institute of Automation,CAS
Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA | |
Li, Gang1,2; Li, Fanrong1,2; Zhao, Tianli1; Cheng, Jian1,2,3 | |
2018 | |
会议名称 | Design, Automation & Test in Europe Conference & Exhibition (DATE) |
会议日期 | 2018 |
会议地点 | Dresden, Germany |
摘要 | FPGA-based CNN accelerators are gaining popularity due to high energy efficiency and great flexibility in recent years. However, as the networks grow in depth and width, the great volume of intermediate data is too large to store on chip, data transfers between on-chip memory and off-chip memory should be frequently executed, which leads to unexpected offchip memory access latency and energy consumption. In this paper, we propose a block convolution approach, which is a memory-efficient, simple yet effective block-based convolution to completely avoid intermediate data from streaming out to off-chip memory during network inference. Experiments on the very large VGG-16 network show that the improved top-1/top-5 accuracy of 72.60%/91.10% can be achieved on the ImageNet classification task with the proposed approach. As a case study, we implement the VGG-16 network with block convolution on Xilinx Zynq ZC706 board, achieving a frame rate of 12.19fps under 150MHz working frequency, with all intermediate data staying on chip. |
URL | 查看原文 |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/48637 |
专题 | 复杂系统认知与决策实验室_高效智能计算与学习 |
通讯作者 | Cheng, Jian |
作者单位 | 1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.CAS Center for Excellence in Brain Science and Intelligence Technology |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Li, Gang,Li, Fanrong,Zhao, Tianli,et al. Block Convolution: Towards Memory-Efficient Inference of Large-Scale CNNs on FPGA[C],2018. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Block_convolution_To(244KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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