图 书 馆
深度压缩：使用剪枝、训练量化和哈夫曼编码对深层神经网络进行压缩Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding |
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| 课程网址: | http://videolectures.net/iclr2016_han_deep_compression/ |
| 主讲教师: | Song Han |
| 开课单位: | 斯坦福大学 |
| 开课时间: | 2016-05-27 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Neural networks are both computationally intensive and memory intensive, making them difficult to deploy on embedded systems with limited hardware resources. To address this limitation, we introduce "deep compression", a three stage pipeline: pruning, trained quantization and Huffman coding, that work together to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy. Our method first prunes the network by learning only the important connections. Next, we quantize the weights to enforce weight sharing, finally, we apply Huffman coding. After the first two steps we retrain the network to fine tune the remaining connections and the quantized centroids. Pruning, reduces the number of connections by 9x to 13x; Quantization then reduces the number of bits that represent each connection from 32 to 5. On the ImageNet dataset, our method reduced the storage required by AlexNet by 35x, from 240MB to 6.9MB, without loss of accuracy. Our method reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of accuracy. This allows fitting the model into on-chip SRAM cache rather than off-chip DRAM memory. Our compression method also facilitates the use of complex neural networks in mobile applications where application size and download bandwidth are constrained. Benchmarked on CPU, GPU and mobile GPU, compressed network has 3x to 4x layerwise speedup and 3x to 7x better energy efficiency. |
| 关 键 词: | 深度压缩; 深层神经网络; 剪枝 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-27：yxd |
| 最后编审: | 2020-11-27：yxd |
| 阅读次数: | 53 |