Abstract
Deep Convolutional Neural Networks (DCNN), a branch of Deep Neural Networks which use the deep graph with multiple processing layers, enables the convolutional model to finely abstract the high-level features behind an image. Large-scale applications using DCNN mainly operate in high-performance server clusters, GPUs or FPGA clusters; it is restricted to extend the applications onto mobile/wearable devices and Internet-of-Things (IoT) entities due to high power/energy consumption. Stochastic Computing is a promising method to overcome this shortcoming used in specific hardware-based systems. Many complex arithmetic operations can be implemented with very simple hardware logic in the SC framework, which alleviates the extensive computation complexity. The exploration of network-wise optimization and the revision of network structure with respect to stochastic computing based hardware design have not been discussed in previous work. In this paper, we investigate Deep Stochastic Convolutional Neural Network (DSCNN) for DCNN using stochastic computing. The essential calculation components using SC are designed and evaluated. We propose a joint optimization method to collaborate components guaranteeing a high calculation accuracy in each stage of the network. The structure of original DSCNN is revised to accommodate SC hardware design's simplicity. Experimental Results show that as opposed to software inspired feature extraction block in DSCNN, an optimized hardware oriented feature extraction block achieves as higher as 59.27% calculation precision. And the optimized DSCNN can achieve only 3.48% network test error rate compared to 27.83% for baseline DSCNN using software inspired feature extraction block.
Original language | English (US) |
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Title of host publication | Proceedings of the 34th IEEE International Conference on Computer Design, ICCD 2016 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 678-681 |
Number of pages | 4 |
ISBN (Electronic) | 9781509051427 |
DOIs | |
State | Published - Nov 22 2016 |
Event | 34th IEEE International Conference on Computer Design, ICCD 2016 - Scottsdale, United States Duration: Oct 2 2016 → Oct 5 2016 |
Other
Other | 34th IEEE International Conference on Computer Design, ICCD 2016 |
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Country/Territory | United States |
City | Scottsdale |
Period | 10/2/16 → 10/5/16 |
Keywords
- Deep Convolutional Neural Networks
- Deep Learning
- Hardware-oriented Co-optimization
- Stochastic Computing
ASJC Scopus subject areas
- Hardware and Architecture