The papers in this special section examine neuromorphic photonics which combines optical physics and unconventional computing, resulting in a new class of ultrafast information processors for neuromorphic information and signal processing, machine learning, and high-performance computing. These processors can enable applications where low latency, high bandwidth, and low switching energies are paramount. Fundamentally, such computing concepts heavily depend on interconnects, a functionality where photonic processors can significantly outperform electronic systems. By combining the high bandwidth and efficiency of photonic devices with the adaptive, parallelism and complexity similar to the brain, photonic neural networks have the potential to be faster than conventional neural networks, while consuming less energy.

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用于深度学习和神经计算的光子学 JSTQE 问题简介

这个特殊部分的论文研究了神经形态光子学，它结合了光学物理和非常规计算，产生了一类用于神经形态信息和信号处理、机器学习和高性能计算的新型超快信息处理器。这些处理器可以支持低延迟、高带宽和低开关能量至关重要的应用。从根本上说，这样的计算概念在很大程度上依赖于互连，这是光子处理器可以显着优于电子系统的功能。通过将光子设备的高带宽和效率与类似于大脑的自适应、并行性和复杂性相结合，光子神经网络有可能比传统神经网络更快，同时消耗更少的能量。