Name
Title | ||
|---|---|---|
A Block-Floating-Point Arithmetic Based FPGA Accelerator for Convolutional Neural Networks |
Abstract | ||
|---|---|---|
Convolutional neural networks (CNNs) have been widely used in computer vision applications and achieved great success. However, large-scale CNN models usually consume a lot of computing and memory resources, which makes it difficult for them to be deployed on embedded devices. An efficient block-floating-point (BFP) arithmetic is proposed in this paper. compared with 32-bit floating-point arithmetic, the memory and off-chip bandwidth requirements during convolution are reduced by 50% and 72.37%, respectively. Due to the adoption of BFP arithmetic, the complex multiplication and addition operations of floating-point numbers can be replaced by the corresponding operations of fixed-point numbers, which is more efficient on hardware. A CNN model can be deployed on our accelerator with no more than 0.14% top-1 accuracy loss, and there is no need for retraining and fine-tuning. By employing a series of ping-pong memory access schemes, 2-dimensional propagate partial multiply-accumulate (PPMAC) processors, and an optimized memory system, we implemented a CNN accelerator on Xilinx VC709 evaluation board. The accelerator achieves a performance of 665.54 GOP/s and a power efficiency of 89.7 GOP/s/W under a 300 MHz working frequency, which outperforms previous FPGA based accelerators significantly. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/GlobalSIP45357.2019.8969292 | 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP) |
Keywords | Field | DocType |
CNN,FPGA,block-floating-point | Electrical efficiency,Convolutional neural network,Computer science,Convolution,Block floating-point,Field-programmable gate array,Arithmetic,Bandwidth (signal processing),Complex multiplication | Conference |
ISSN | ISBN | Citations |
2376-4066 | 978-1-7281-2724-8 | 0 |
PageRank | References | Authors |
0.34 | 8 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Heshan Zhang | 1 | 0 | 0.34 |
| Zhenyu Liu | 2 | 58 | 12.62 |
| Guanwen Zhang | 3 | 31 | 8.63 |
| Jiwu Dai | 4 | 0 | 0.34 |
| Xiaocong Lian | 5 | 25 | 5.13 |
| Wei Zhou | 6 | 17 | 9.47 |
| Xiangyang Ji | 7 | 533 | 73.14 |