S
Song Han
Researcher at Massachusetts Institute of Technology
Publications - 144
Citations - 39596
Song Han is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Artificial neural network & Deep learning. The author has an hindex of 48, co-authored 134 publications receiving 28364 citations. Previous affiliations of Song Han include University of Chicago & Stanford University.
Papers
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Proceedings Article
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
TL;DR: Deep Compression as mentioned in this paper proposes a three-stage pipeline: pruning, quantization, and Huffman coding to reduce the storage requirement of neural networks by 35x to 49x without affecting their accuracy.
Posted Content
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
TL;DR: This work proposes a small DNN architecture called SqueezeNet, which achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters and is able to compress to less than 0.5MB (510x smaller than AlexNet).
Proceedings Article
Learning both weights and connections for efficient neural networks
TL;DR: In this paper, the authors proposed a method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections using a three-step method.
Journal ArticleDOI
EIE: efficient inference engine on compressed deep neural network
TL;DR: In this paper, the authors proposed an energy efficient inference engine (EIE) that performs inference on a compressed network model and accelerates the resulting sparse matrix-vector multiplication with weight sharing.
Posted Content
Learning both Weights and Connections for Efficient Neural Networks
TL;DR: A method to reduce the storage and computation required by neural networks by an order of magnitude without affecting their accuracy by learning only the important connections, and prunes redundant connections using a three-step method.