The field of mimicking the structure of the brain on a chip is experiencing interest driven by the demand for machine intelligent applications. However, the power consumption and available performance of machine-learning (ML) accelerating hardware still leave much desire for improvement. In this letter, we share viewpoints, challenges, and prospects of electronic-photonic neural network (NN) accelerators. Combining electronics with photonics offers synergistic co-design strategies for high-performance AI Application-specific integrated circuits (ASICs) and systems. Taking advantages of photonic signal processing capabilities and combining them with electronic logic control and data storage is an emerging prospect. However, the optical component library leaves much to be desired and is challenged by the enormous size of photonic devices. Within this context, we will review the emerging electro-optic materials, functional devices, and systems packaging strategies that, when realized, provide significant performance gains and fuel the ongoing AI revolution, leading to a stand-alone photonics-inside AI ASIC ‘black-box’ for streamlined plug-and-play board integration in future AI processors.
All Science Journal Classification (ASJC) codes
- Electronic, Optical and Magnetic Materials