ISCA'18 Prediction based execution on Deep Neural Networks
提出了一種基於預測（prediction based）的DNN執行模型，無精度損失。主要預測做法是在計算時對I或W的高低位拆分，利用高位預測，利用低位計算。 USPE：統一的串行PE，解決了高低位寬度不同的問題，減小面積開銷 scale-out Design：我的理解是探索合適的複用方法，既能有比較大的利用率，又能使得memory access不是瓶頸。 圖1：理論上可以跳過的0。我對此表示疑惑，
相關文章
- 1. comments on"An Experimental Study on Speech Enhancement Based on Deep Neural Networks"(一)
- 2. An Experimental Study on Speech Enhancement Based on Deep Neural Networks學習摘要
- 3. Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning
- 4. Weighted-Entropy-based Quantization for Deep Neural Networks
- 5. DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices
- 6. Inductive Matrix Completion Based on Graph Neural Networks
- 7. 13、Probabilistic Short-term Wind Power Forecasting Based on Deep Neural Networks
- 8. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 9. CVPR 2017 On human motion prediction using recurrent neural networks 論文解讀
- 10. Improving Deep Neural Networks
- 更多相關文章...
- • Docker 安裝 MySQL - Docker教程
- • Docker 安裝 MongoDB - Docker教程
- • Flink 數據傳輸及反壓詳解
- • RxJava操作符（一）Creating Observables
相關標籤/搜索
每日一句
-
每一个你不满意的现在，都有一个你没有努力的曾经。
最新文章
歡迎關注本站公眾號,獲取更多信息
相關文章
- 1. comments on"An Experimental Study on Speech Enhancement Based on Deep Neural Networks"(一)
- 2. An Experimental Study on Speech Enhancement Based on Deep Neural Networks學習摘要
- 3. Flow Prediction in Spatio-Temporal Networks Based on Multitask Deep Learning
- 4. Weighted-Entropy-based Quantization for Deep Neural Networks
- 5. DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices
- 6. Inductive Matrix Completion Based on Graph Neural Networks
- 7. 13、Probabilistic Short-term Wind Power Forecasting Based on Deep Neural Networks
- 8. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 9. CVPR 2017 On human motion prediction using recurrent neural networks 論文解讀
- 10. Improving Deep Neural Networks