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：

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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。