| Autor: |
Robert A. Cohen, Hyomin Choi, Ivan V. Bajic |
| Jazyk: |
angličtina |
| Rok vydání: |
2021 |
| Předmět: |
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| Zdroj: |
IEEE Open Journal of Circuits and Systems, Vol 2, Pp 350-362 (2021) |
| Druh dokumentu: |
article |
| ISSN: |
2644-1225 |
| DOI: |
10.1109/OJCAS.2021.3072884 |
| Popis: |
In collaborative intelligence applications, part of a deep neural network (DNN) is deployed on a lightweight device such as a mobile phone or edge device, and the remaining portion of the DNN is processed where more computing resources are available, such as in the cloud. This paper presents a novel lightweight compression technique designed specifically to quantize and compress the features output by the intermediate layer of a split DNN, without requiring any retraining of the network weights. Mathematical models for estimating the clipping and quantization error of leaky-ReLU and ReLU activations at this intermediate layer are used to compute optimal clipping ranges for coarse quantization. A mathematical model for estimating the clipping and quantization error of leaky-ReLU activations at this intermediate layer is developed and used to compute optimal clipping ranges for coarse quantization. We also present a modified entropy-constrained design algorithm for quantizing clipped activations. When applied to popular object-detection and classification DNNs, we were able to compress the 32-bit floating point intermediate activations down to 0.6 to 0.8 bits, while keeping the loss in accuracy to less than 1%. When compared to HEVC, we found that the lightweight codec consistently provided better inference accuracy, by up to 1.3%. The performance and simplicity of this lightweight compression technique makes it an attractive option for coding an intermediate layer of a split neural network for edge/cloud applications. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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