| Autor: |
Karimzadeh, Foroozan, Cao, Ningyuan, Crafton, Brian, Romberg, Justin, Raychowdhury, Arijit |
| Rok vydání: |
2019 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
Deep neural networks (DNNs) have been emerged as the state-of-the-art algorithms in broad range of applications. To reduce the memory foot-print of DNNs, in particular for embedded applications, sparsification techniques have been proposed. Unfortunately, these techniques come with a large hardware overhead. In this paper, we present a hardware-aware pruning method where the locations of non-zero weights are derived in real-time from a Linear Feedback Shift Registers (LFSRs). Using the proposed method, we demonstrate a total saving of energy and area up to 63.96% and 64.23% for VGG-16 network on down-sampled ImageNet, respectively for iso-compression-rate and iso-accuracy. |
| Databáze: |
arXiv |
| Externí odkaz: |
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