Architecture-Level Modeling of Photonic Deep Neural Network Accelerators
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910679168974848 |
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| author | Andrulis, Tanner Chaudhry, Gohar Irfan Suriyakumar, Vinith M. Emer, Joel S. Sze, Vivienne |
| author_facet | Andrulis, Tanner Chaudhry, Gohar Irfan Suriyakumar, Vinith M. Emer, Joel S. Sze, Vivienne |
| contents | Photonics is a promising technology to accelerate Deep Neural Networks as it can use optical interconnects to reduce data movement energy and it enables low-energy, high-throughput optical-analog computations. To realize these benefits in a full system (accelerator + DRAM), designers must ensure that the benefits of using the electrical, optical, analog, and digital domains exceed the costs of converting data between domains. Designers must also consider system-level energy costs such as data fetch from DRAM. Converting data and accessing DRAM can consume significant energy, so to evaluate and explore the photonic system space, there is a need for a tool that can model these full-system considerations. In this work, we show that similarities between Compute-in-Memory (CiM) and photonics let us use CiM system modeling tools to accurately model photonics systems. Bringing modeling tools to photonics enables evaluation of photonic research in a full-system context, rapid design space exploration, co-design, and comparison between systems. Using our open-source model, we show that cross-domain conversion and DRAM can consume a significant portion of photonic system energy. We then demonstrate optimizations that reduce conversions and DRAM accesses to improve photonic system energy efficiency by up to 3x. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_07266 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Architecture-Level Modeling of Photonic Deep Neural Network Accelerators Andrulis, Tanner Chaudhry, Gohar Irfan Suriyakumar, Vinith M. Emer, Joel S. Sze, Vivienne Emerging Technologies Hardware Architecture Photonics is a promising technology to accelerate Deep Neural Networks as it can use optical interconnects to reduce data movement energy and it enables low-energy, high-throughput optical-analog computations. To realize these benefits in a full system (accelerator + DRAM), designers must ensure that the benefits of using the electrical, optical, analog, and digital domains exceed the costs of converting data between domains. Designers must also consider system-level energy costs such as data fetch from DRAM. Converting data and accessing DRAM can consume significant energy, so to evaluate and explore the photonic system space, there is a need for a tool that can model these full-system considerations. In this work, we show that similarities between Compute-in-Memory (CiM) and photonics let us use CiM system modeling tools to accurately model photonics systems. Bringing modeling tools to photonics enables evaluation of photonic research in a full-system context, rapid design space exploration, co-design, and comparison between systems. Using our open-source model, we show that cross-domain conversion and DRAM can consume a significant portion of photonic system energy. We then demonstrate optimizations that reduce conversions and DRAM accesses to improve photonic system energy efficiency by up to 3x. |
| title | Architecture-Level Modeling of Photonic Deep Neural Network Accelerators |
| topic | Emerging Technologies Hardware Architecture |
| url | https://arxiv.org/abs/2405.07266 |