Optical and Electrical Memories for Analog Optical Computing
Optical and Electrical Memories for Analog Optical Computing
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DOI：
10.1109/jstqe.2023.3239918
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发表时间：
2023-03
影响因子：
4.9
通讯作者：
S. R. Kari;Carlos A. Ríos Ocampo;Lei Jiang;Jiawei Meng;N. Peserico;V. Sorger;Juejun Hu;N. Youngblood
中科院分区：
文献类型：
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作者：
S. R. Kari;Carlos A. Ríos Ocampo;Lei Jiang;Jiawei Meng;N. Peserico;V. Sorger;Juejun Hu;N. Youngblood
人工智能（AI）领域最近取得成功的关键是能够训练越来越多的参数，这些参数在非线性节点层之间形成固定的连接矩阵。从历史上看，这种“深度学习”的人工智能方法需要处理能力的指数增长，这远远超过了数字硬件计算吞吐量的增长以及处理效率的趋势。因此，需要新的计算范例来实现信息的有效处理，同时大幅提高计算吞吐量。光子域中模拟计算的新兴策略有可能大大减少延迟，但需要根据神经网络的学习参数修改光学处理元件的能力。在这篇观点文章中，我们提供了一个前瞻性的观点，即在人工智能的背景下，将光存储器和电存储器耦合到集成光子硬件。我们还表明，对于编程存储器，光子随机存取存储器（PRAM）的读取能量延迟产品可以是数量级低于电子SRAM。我们的目的是概述PRAM成为未来铸造工艺不可或缺的一部分的路径，并为新兴的AI硬件提供这些有前途的设备。
Key to recent successes in the field of artificial intelligence (AI) has been the ability to train a growing number of parameters which form fixed connectivity matrices between layers of nonlinear nodes. This “deep learning” approach to AI has historically required an exponential growth in processing power which far exceeds the growth in computational throughput of digital hardware as well as trends in processing efficiency. New computing paradigms are therefore required to enable efficient processing of information while drastically improving computational throughput. Emerging strategies for analog computing in the photonic domain have the potential to drastically reduce latency but require the ability to modify optical processing elements according to the learned parameters of the neural network. In this point-of-view article, we provide a forward-looking perspective on both optical and electrical memories coupled to integrated photonic hardware in the context of AI. We also show that for programmed memories, the READ energy-latency-product of photonic random-access memory (PRAM) can be orders of magnitude lower compared to electronic SRAMs. Our intent is to outline path for PRAMs to become an integral part of future foundry processes and give these promising devices relevance for emerging AI hardware.