Reuse and Blend: Energy-Efficient Optical Neural Network Enabled by Weight Sharing
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866917771201216512 |
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| author | Xu, Bo Fang, Yuetong Yu, Shaoliang Xu, Renjing |
| author_facet | Xu, Bo Fang, Yuetong Yu, Shaoliang Xu, Renjing |
| contents | Optical neural networks (ONN) based on micro-ring resonators (MRR) have emerged as a promising alternative to significantly accelerating the massive matrix-vector multiplication (MVM) operations in artificial intelligence (AI) applications. However, the limited scale of MRR arrays presents a challenge for AI acceleration. The disparity between the small MRR arrays and the large weight matrices in AI necessitates extensive MRR writings, including reprogramming and calibration, resulting in considerable latency and energy overheads. To address this problem, we propose a novel design methodology to lessen the need for frequent weight reloading. Specifically, we propose a reuse and blend (R&B) architecture to support efficient layer-wise and block-wise weight sharing, which allows weights to be reused several times between layers/blocks. Experimental results demonstrate the R&B system can maintain comparable accuracy with 69% energy savings and 57% latency improvement. These results highlight the promise of the R&B to enable the efficient deployment of advanced deep learning models on photonic accelerators. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01836 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Reuse and Blend: Energy-Efficient Optical Neural Network Enabled by Weight Sharing Xu, Bo Fang, Yuetong Yu, Shaoliang Xu, Renjing Hardware Architecture Optical neural networks (ONN) based on micro-ring resonators (MRR) have emerged as a promising alternative to significantly accelerating the massive matrix-vector multiplication (MVM) operations in artificial intelligence (AI) applications. However, the limited scale of MRR arrays presents a challenge for AI acceleration. The disparity between the small MRR arrays and the large weight matrices in AI necessitates extensive MRR writings, including reprogramming and calibration, resulting in considerable latency and energy overheads. To address this problem, we propose a novel design methodology to lessen the need for frequent weight reloading. Specifically, we propose a reuse and blend (R&B) architecture to support efficient layer-wise and block-wise weight sharing, which allows weights to be reused several times between layers/blocks. Experimental results demonstrate the R&B system can maintain comparable accuracy with 69% energy savings and 57% latency improvement. These results highlight the promise of the R&B to enable the efficient deployment of advanced deep learning models on photonic accelerators. |
| title | Reuse and Blend: Energy-Efficient Optical Neural Network Enabled by Weight Sharing |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2409.01836 |