Name
Abstract | ||
|---|---|---|
This paper describes a Super-Skinny Convolutional Neural Network (SS-CNN) and its implementation on a Cyclone IVE field programmable gate array (FPGA), for handwritten digit recognition. This SS-CNN performs state-of-the-art recognition accuracy but with fewer layers and less neurons. Using parameters with 8 bits of precision, the FPGA solutions of this SS-CNN show no recognition accuracy loss when compared to the 32-bit floating point software solution. In addition to high recognition accuracy, both of the proposed FPGA solutions are low power and require little FPGA area. The proposed hardware solutions indicate a 67 to 355 times power savings potential when compared to the software solution. Thus, our SS-CNN provides a high-performance, low computation demands, hardware friendly, and power efficient solution. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/UEMCON47517.2019.8992928 | 2019 IEEE 10th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) |
Keywords | Field | DocType |
SS-CNN,FPGA,MNIST,deep learning,machine learning,hardware acceleration | MNIST database,Computer science,Convolutional neural network,Floating point,Field-programmable gate array,Human–computer interaction,Software,Artificial intelligence,Hardware acceleration,Deep learning,Computer hardware,Computation | Conference |
ISBN | Citations | PageRank |
978-1-7281-3886-2 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiong Si | 1 | 0 | 0.34 |
| Evangelos A. Yfantis | 2 | 16 | 10.80 |
| Sarah Harris | 3 | 0 | 1.69 |