Name
Title | ||
|---|---|---|
An energy-efficient deep learning processor with heterogeneous multi-core architecture for convolutional neural networks and recurrent neural networks |
Abstract | ||
|---|---|---|
An energy-efficient deep learning processor is proposed for convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in mobile platforms. The 16mm
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chip is fabricated using 65nm technology with 3 key features, 1) Reconfigurable heterogeneous architecture to support both CNNs and RNNs, 2) LUT-based reconfigurable multiplier optimized for dynamic fixed-point with the on-line adaptation, 3) Quantization table-based matrix multiplication to reduce off-chip memory access and remove duplicated multiplications. As a result, compared to the [2] and [3], this work shows 20× and 4.5× higher energy efficiency, respectively. Also, DNPU shows 6.5× higher energy efficiency compared to the [5]. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/CoolChips.2017.7946376 | 2017 IEEE Symposium in Low-Power and High-Speed Chips (COOL CHIPS) |
Keywords | DocType | ISSN |
deep learning,convolutional neural network,recurrent neural network,heterogeneous,LUT | Conference | 2473-4683 |
ISBN | Citations | PageRank |
978-1-5386-3829-3 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dongjoo Shin | 1 | 67 | 11.02 |
| Jinmook Lee | 2 | 65 | 9.34 |
| Jinsu Lee | 3 | 23 | 7.89 |
| Juhyoung Lee | 4 | 3 | 1.40 |
| Hoi-Jun Yoo | 5 | 1574 | 226.79 |