تقرير
Retrospective: EIE: Efficient Inference Engine on Sparse and Compressed Neural Network
| العنوان: | Retrospective: EIE: Efficient Inference Engine on Sparse and Compressed Neural Network |
|---|---|
| المؤلفون: | Han, Song, Liu, Xingyu, Mao, Huizi, Pu, Jing, Pedram, Ardavan, Horowitz, Mark A., Dally, William J. |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Hardware Architecture |
| الوصف: | EIE proposed to accelerate pruned and compressed neural networks, exploiting weight sparsity, activation sparsity, and 4-bit weight-sharing in neural network accelerators. Since published in ISCA'16, it opened a new design space to accelerate pruned and sparse neural networks and spawned many algorithm-hardware co-designs for model compression and acceleration, both in academia and commercial AI chips. In retrospect, we review the background of this project, summarize the pros and cons, and discuss new opportunities where pruning, sparsity, and low precision can accelerate emerging deep learning workloads. Comment: Invited retrospective paper at ISCA 2023 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2306.09552 |
| رقم الأكسشن: | edsarx.2306.09552 |
| قاعدة البيانات: | arXiv |
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