Abstract Photonics offers exciting opportunities for neuromorphic computing. This paper specifically reviews the prospects of integrated optical solutions for accelerating inference and training of artificial neural networks. Calculating the synaptic function, thereof, is computationally very expensive and does not scale well on state-of-the-art computing platforms. Analog signal processing, using linear and nonlinear properties of integrated optical devices, offers a path toward substantially improving performance and power efficiency of these artificial intelligence workloads. The ability of integrated photonics to operate at very high speeds opens opportunities for time-critical real-time applications, while chip-level integration paves the way to cost-effective manufacturing and assembly.

中文翻译：

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集成光子神经网络的机会

Abstract Photonics 为神经形态计算提供了令人兴奋的机会。本文具体回顾了用于加速人工神经网络推理和训练的集成光学解决方案的前景。计算突触函数在计算上非常昂贵并且在最先进的计算平台上不能很好地扩展。模拟信号处理利用集成光学器件的线性和非线性特性，为大幅提高这些人工智能工作负载的性能和功率效率提供了途径。集成光子学以非常高的速度运行的能力为时间关键的实时应用开辟了机会，而芯片级集成为具有成本效益的制造和组装铺平了道路。