Feb. 13, 2024, 5:44 a.m. | Meng Zhang Dennis Yin Nicholas Gangi Amir Begovi\'c Alexander Chen Zhaoran Rena Huang Jiaqi Gu

cs.LG updates on arXiv.org arxiv.org

Electronic-photonic computing systems offer immense potential in energy-efficient artificial intelligence (AI) acceleration tasks due to the superior computing speed and efficiency of optics, especially for real-time, low-energy deep neural network (DNN) inference tasks on resource-restricted edge platforms. However, current optical neural accelerators based on foundry-available devices and conventional system architecture still encounter a performance gap compared to highly customized electronic counterparts. To bridge the performance gap due to lack of domain specialization, we present a time-multiplexed dynamic photonic tensor accelerator, …

accelerators artificial artificial intelligence computing computing systems core cs.ai cs.et cs.lg current deep neural network dnn dynamic edge edge ai efficiency electronic energy foundry inference intelligence light low low-energy network neural network optic optical optics photonic computing platforms real-time speed systems tasks tensor

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