HAQ: Hardware-Aware Automated Quantization with Mixed Precision
本文的創新點爲： 作者認爲，在不同的平臺上，以及不同的layer上，合適的量化bit數都是不同的。但是如果想認爲找到這個最優值是不可能的，於是作者使用強化學習的方法來找到最合理的量化bit數。 本來芯片是不支持混合精度的計算的，但隨着硬件的發展，支持混合機精度計算的芯片也出現了：比如蘋果的A12，NVIDIA的圖靈GPU框架等，從而使得這種思路得以實現。 它的優點是完全自動化，不需要專家知識和啓發
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相關文章
- 1. Training With Mixed Precision
- 2. INCREMENTAL NETWORK QUANTIZATION: TOWARDS LOSSLESS CNNS WITH LOW-PRECISION WEIGHTS
- 3. Paper：《High-precision automated reconstruction of neurons with flood-filling networks》
- 4. Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights論文
- 5. INQ 論文解讀：Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights
- 6. 論文翻譯：Deep Learning with Low Precision by Half-wave Gaussian Quantization
- 7. iOS Automated Tests with UIAutomation
- 8. iOS Automated Testing With Calabash, Cucumber
- 9. Docker Hub Automated Build with GitHub
- 10. 神經網絡壓縮（8）Incremental Network Quantization: Towards Lossless CNNs with Low-precision Weights