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CrossLight: A Cross-Layer Optimized Silicon Photonic Neural Network Accelerator
arXiv - CS - Emerging Technologies Pub Date : 2021-02-13 , DOI: arxiv-2102.06960 Febin Sunny, Asif Mirza, Mahdi Nikdast, Sudeep Pasricha
arXiv - CS - Emerging Technologies Pub Date : 2021-02-13 , DOI: arxiv-2102.06960 Febin Sunny, Asif Mirza, Mahdi Nikdast, Sudeep Pasricha
Domain-specific neural network accelerators have seen growing interest in
recent years due to their improved energy efficiency and inference performance
compared to CPUs and GPUs. In this paper, we propose a novel cross-layer
optimized neural network accelerator called CrossLight that leverages silicon
photonics. CrossLight includes device-level engineering for resilience to
process variations and thermal crosstalk, circuit-level tuning enhancements for
inference latency reduction, and architecture-level optimization to enable
higher resolution, better energy-efficiency, and improved throughput. On
average, CrossLight offers 9.5x lower energy-per-bit and 15.9x higher
performance-per-watt at 16-bit resolution than state-of-the-art photonic deep
learning accelerators.
中文翻译:
CrossLight:跨层优化的硅光子神经网络加速器
由于与CPU和GPU相比,特定领域的神经网络加速器具有更高的能效和推理性能,因此近年来引起了越来越多的关注。在本文中,我们提出了一种新型的跨层优化神经网络加速器,称为CrossLight,它利用了硅光子学。CrossLight包括用于处理变化和热串扰的设备级工程设计,用于推理延迟减少的电路级调整增强功能以及用于实现更高的分辨率,更好的能源效率和提高的吞吐量的体系结构级优化。平均而言,与最先进的光子深度学习加速器相比,CrossLight在16位分辨率下的单位能量能耗低9.5倍,每瓦性能提高15.9倍。
更新日期:2021-02-16
中文翻译:
CrossLight:跨层优化的硅光子神经网络加速器
由于与CPU和GPU相比,特定领域的神经网络加速器具有更高的能效和推理性能,因此近年来引起了越来越多的关注。在本文中,我们提出了一种新型的跨层优化神经网络加速器,称为CrossLight,它利用了硅光子学。CrossLight包括用于处理变化和热串扰的设备级工程设计,用于推理延迟减少的电路级调整增强功能以及用于实现更高的分辨率,更好的能源效率和提高的吞吐量的体系结构级优化。平均而言,与最先进的光子深度学习加速器相比,CrossLight在16位分辨率下的单位能量能耗低9.5倍,每瓦性能提高15.9倍。