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| Content Provider | Directory of Open Access Journals (DOAJ) |
|---|---|
| Author | Tomer Malach Shlomo Greenberg Moshe Haiut |
| Abstract | Deep Neural Networks (DNN) are widely applied to many mobile applications demanding real-time implementation and large memory space. Therefore, it presents a new challenge for low-power and efficient implementation of a diversity of applications, such as speech recognition and image classification, for embedded edge devices. This work presents a hardware-based DNN compression approach to address the limited memory resources in edge devices. We propose a new entropy-based compression algorithm for encoding DNN weights, as well as a real-time decoding method and efficient dedicated hardware implementation. The proposed approach enables a significant reduction of the required DNN weights memory (approximately 70% and 63% for AlexNet and VGG19, respectively), while allowing the decoding of one weight per clock cycle. Results show a high compression ratio compared to well-known lossless compression algorithms. The proposed hardware decoder enables an efficient implementation of large DNN networks in low-power edge devices with limited memory resources. |
| e-ISSN | 21693536 |
| DOI | 10.1109/ACCESS.2020.3037254 |
| Journal | IEEE Access |
| Volume Number | 8 |
| Language | English |
| Publisher | IEEE |
| Publisher Date | 2020-01-01 |
| Publisher Place | United States |
| Access Restriction | Open |
| Subject Keyword | Electrical Engineering. Electronics. Nuclear Engineering Deep Neural Network Entropy Compression Hardware Decoder Real-time |
| Content Type | Text |
| Resource Type | Article |
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