论文标题
Netcast：WDM定义的光学神经网络的低功率边缘计算
Netcast: Low-Power Edge Computing with WDM-defined Optical Neural Networks
论文作者
论文摘要
本文分析了Netcast的性能和能源效率，Netcast是一个最近提出的光学神经网络结构架构，设计用于边缘计算。 Netcast performs deep neural network inference by dividing the computational task into two steps, which are split between the server and (edge) client: (1) the server employs a wavelength-multiplexed modulator array to encode the network's weights onto an optical signal in an analog time-frequency basis, and (2) the client obtains the desired matrix-vector product through modulation and time-integrated detection.通过有效将能量和内存要求返回服务器，可以同时使用波长多路复用，宽带调制和集成检测，从而可以在客户端运行大型神经网络。性能和能源效率从根本上受到串扰和检测器噪声的限制。我们得出这些限制的分析表达式，并执行数值模拟以验证这些界限。
This paper analyzes the performance and energy efficiency of Netcast, a recently proposed optical neural-network architecture designed for edge computing. Netcast performs deep neural network inference by dividing the computational task into two steps, which are split between the server and (edge) client: (1) the server employs a wavelength-multiplexed modulator array to encode the network's weights onto an optical signal in an analog time-frequency basis, and (2) the client obtains the desired matrix-vector product through modulation and time-integrated detection. The simultaneous use of wavelength multiplexing, broadband modulation, and integration detection allows large neural networks to be run at the client by effectively pushing the energy and memory requirements back to the server. The performance and energy efficiency are fundamentally limited by crosstalk and detector noise, respectively. We derive analytic expressions for these limits and perform numerical simulations to verify these bounds.
