Multi-channel precision-sparsity-adapted inter-frame differential data codec for video neural network processor
| Autor: | Zhuqing Yuan, Fanyang Cheng, Zhe Yuan, Yongpan Liu, Fang Su, Yixiong Yang, Huazhong Yang |
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| Rok vydání: | 2020 |
| Předmět: |
Artificial neural network
Computer science business.industry 020208 electrical & electronic engineering Inter frame Data compression ratio Data_CODINGANDINFORMATIONTHEORY 02 engineering and technology ENCODE 020202 computer hardware & architecture Encoding (memory) 0202 electrical engineering electronic engineering information engineering Codec business Computer hardware Communication channel Coding (social sciences) |
| Zdroj: | ISLPED |
| Popis: | Activation I/O traffic is a critical bottleneck of video neural network processor. Recent works adopted an inter-frame difference method to reduce activation size. However, current methods can't fully adapt to the various precision and sparsity in differential data. In this paper, we propose the multi-channel precision-sparsity-adapted codec, which will separate the differential activation and encode activation in multiple channels. We analyze the most adapted encoding of each channel, and select the optimal channel number with the best performance. A two-channel codec hardware has been implemented in the ASIC accelerator, which can encode/decode activations in parallel. Experiment results show that our coding achieves 2.2x-18.2x compression rate in three scenarios with no accuracy loss, and the hardware has 42x/174x improvement on speed and energy-efficiency compared with the software codec. |
| Databáze: | OpenAIRE |
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