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1. Optical Comb-Based Monolithic Photonic-Electronic Accelerators for Self-Attention Computation

   https://doi.org/10.1109/JSTQE.2024.3425456  

   Hsueh, Tzu-Chien; Fainman, Yeshaiahu; Lin, Bill (July 2024, IEEE Journal of Selected Topics in Quantum Electronics)

   Capmany, José (Ed.)

   This paper adopts advanced monolithic silicon-photonics integrated-circuits manufacturing capabilities to realize system-on-chip photonic-electronic linear-algebra accelerators for self-attention computation in various applications of deep-learning neural networks and Large Language Models. With the features of holistic co-design approaches, optical comb-based broadband modulations, and consecutive matrix-multiplication architecture, the system/circuit/device-level simulations of the proposed accelerator can achieve 2.14-TMAC/s/mm2 computation density and 27.9-fJ/MAC energy efficiency with practical considerations of power/area overhead due to photonic-electronic on-chip conversions, integrations, and calibrations. 

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2. Photonic (computational) memories: tunable nanophotonics for data storage and computing

   https://doi.org/10.1515/nanoph-2022-0089  

   Lian, Chuanyu; Vagionas, Christos; Alexoudi, Theonitsa; Pleros, Nikos; Youngblood, Nathan; Ríos, Carlos (May 2022, Nanophotonics)

   Abstract The exponential growth of information stored in data centers and computational power required for various data-intensive applications, such as deep learning and AI, call for new strategies to improve or move beyond the traditional von Neumann architecture. Recent achievements in information storage and computation in the optical domain, enabling energy-efficient, fast, and high-bandwidth data processing, show great potential for photonics to overcome the von Neumann bottleneck and reduce the energy wasted to Joule heating. Optically readable memories are fundamental in this process, and while light-based storage has traditionally (and commercially) employed free-space optics, recent developments in photonic integrated circuits (PICs) and optical nano-materials have opened the doors to new opportunities on-chip. Photonic memories have yet to rival their electronic digital counterparts in storage density; however, their inherent analog nature and ultrahigh bandwidth make them ideal for unconventional computing strategies. Here, we review emerging nanophotonic devices that possess memory capabilities by elaborating on their tunable mechanisms and evaluating them in terms of scalability and device performance. Moreover, we discuss the progress on large-scale architectures for photonic memory arrays and optical computing primarily based on memory performance. 

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3. Waveform inversion via reduced order modeling

   https://doi.org/10.1190/GEO2022-0070.1  

   Borcea, Liliana; Garnier, Josselin; Mamonov, Alexander V.; Zimmerling, Jörn (March 2023, GEOPHYSICS)

   We introduce a novel approach to waveform inversion based on a data-driven reduced order model (ROM) of the wave operator. The presentation is for the acoustic wave equation, but the approach can be extended to elastic or electromagnetic waves. The data are time resolved measurements of the pressure wave gathered by an acquisition system that probes the unknown medium with pulses and measures the generated waves. We propose to solve the inverse problem of velocity estimation by minimizing the square misfit between the ROM computed from the recorded data and the ROM computed from the modeled data, at the current guess of the velocity. We give a step by step computation of the ROM, which depends nonlinearly on the data and yet can be obtained from them in a noniterative fashion, using efficient methods from linear algebra. We also explain how to make the ROM robust to data inaccuracy. The ROM computation requires the full array response matrix gathered with colocated sources and receivers. However, we find that the computation can deal with an approximation of this matrix, obtained from towed-streamer data using interpolation and reciprocity on-the-fly. Although the full-waveform inversion approach of nonlinear least-squares data fitting is challenging without low-frequency information, due to multiple minima of the data fit objective function, we find that the ROM misfit objective function has better behavior, even for a poor initial guess. We also find by explicit computation of the objective functions in a simple setting that the ROM misfit objective function has convexity properties, whereas the least-squares data fit objective function displays multiple local minima. 

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4. Self-controlling photonic-on-chip networks with deep reinforcement learning

   https://doi.org/10.1038/s41598-021-02583-7  

   Do, Nguyen; Truong, Dung; Nguyen, Duy; Hoai, Minh; Pham, Cuong (December 2021, Scientific Reports)

   Abstract We present a novel photonic chip design for high bandwidth four-degree optical switches that support high-dimensional switching mechanisms with low insertion loss and low crosstalk in a low power consumption level and a short switching time. Such four-degree photonic chips can be used to build an integrated full-grid Photonic-on-Chip Network (PCN). With four distinct input/output directions, the proposed photonic chips are superior compared to the current bidirectional photonic switches, where a conventionally sizable PCN can only be constructed as a linear chain of bidirectional chips. Our four-directional photonic chips are more flexible and scalable for the design of modern optical switches, enabling the construction of multi-dimensional photonic chip networks that are widely applied for intra-chip communication networks and photonic data centers. More noticeably, our photonic networks can be self-controlling with our proposed Multi-Sample Discovery model, a deep reinforcement learning model based on Proximal Policy Optimization. On a PCN, we can optimize many criteria such as transmission loss, power consumption, and routing time, while preserving performance and scaling up the network with dynamic changes. Experiments on simulated data demonstrate the effectiveness and scalability of the proposed architectural design and optimization algorithm. Perceivable insights make the constructed architecture become the self-controlling photonic-on-chip networks. 

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5. Nonvolatile Electrically Reconfigurable Integrated Photonic Switch Enabled by a Silicon PIN Diode Heater

   https://doi.org/10.1002/adma.202001218  

   Zheng, Jiajiu; Fang, Zhuoran; Wu, Changming; Zhu, Shifeng; Xu, Peipeng; Doylend, Jonathan_K; Deshmukh, Sanchit; Pop, Eric; Dunham, Scott; Li, Mo; et al (June 2020, Advanced Materials)

   Abstract Reconfigurability of photonic integrated circuits (PICs) has become increasingly important due to the growing demands for electronic–photonic systems on a chip driven by emerging applications, including neuromorphic computing, quantum information, and microwave photonics. Success in these fields usually requires highly scalable photonic switching units as essential building blocks. Current photonic switches, however, mainly rely on materials with weak, volatile thermo‐optic or electro‐optic modulation effects, resulting in large footprints and high energy consumption. As a promising alternative, chalcogenide phase‐change materials (PCMs) exhibit strong optical modulation in a static, self‐holding fashion, but the scalability of present PCM‐integrated photonic applications is still limited by the poor optical or electrical actuation approaches. Here, with phase transitions actuated by in situ silicon PIN diode heaters, scalable nonvolatile electrically reconfigurable photonic switches using PCM‐clad silicon waveguides and microring resonators are demonstrated. As a result, intrinsically compact and energy‐efficient switching units operated with low driving voltages, near‐zero additional loss, and reversible switching with high endurance are obtained in a complementary metal‐oxide‐semiconductor (CMOS)‐compatible process. This work can potentially enable very large‐scale CMOS‐integrated programmable electronic–photonic systems such as optical neural networks and general‐purpose integrated photonic processors. 

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