Name
Abstract | ||
|---|---|---|
Convolutional neural networks (CNNs) have achieved great success in image processing. However, the heavy computational burden it imposes makes it difficult for use in embedded applications that have limited power consumption and performance. Although there are many fast convolution algorithms that can reduce the computational complexity, they increase the difficulty of practical implementation. To overcome these difficulties, this paper proposes several convolution accelerator designs using fast algorithms. The designs are based on the field programmable gate array (FPGA) and display a better balance between the digital signal processor (DSP) and the logic resource, while also requiring lower power consumption. The implementation results show that the power consumption of the accelerator design based on the Strassen-Winograd algorithm is 21.3% less than that of conventional accelerators. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.3390/a12050112 | ALGORITHMS |
Keywords | Field | DocType |
convolutional neural network,fast convolution,FPGA,Strassen,Winograd | Digital signal processing,Convolutional neural network,Convolution,Digital signal processor,Algorithm,Field-programmable gate array,Image processing,Strassen algorithm,Mathematics,Computational complexity theory | Journal |
Volume | Issue | Citations |
12 | 5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yulin Zhao | 1 | 7 | 1.79 |
| Dong-Hui Wang | 2 | 1 | 3.74 |
| Leiou Wang | 3 | 5 | 5.55 |