Energy-Efficient Photonic Spiking Neural Network on a monolithic silicon CMOS photonic platform

Lee, Yun-Jhu; On, Mehmet Berkay; Xiao, Xian; Yoo, S. J. B.

doi:10.1364/ofc.2021.tu5h.5

Cited by 2 publications

(1 citation statement)

References 27 publications

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“…A variety of non-von Neumann computing paradigms have been reported, including quantum computation, , memcomputing, , neuromorphic computing, molecular computing, − optical computing, , etc. On the long journey of seeking a powerful computing paradigm, light-based computation showcases as an active player.…”

Section: Introductionmentioning

confidence: 99%

Integrated Photonic Computing beyond the von Neumann Architecture

Jin

2023

ACS Photonics

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In the context of a doomed end of the Moore's law, various new types of computing architectures have been emerging, aiming to meet the demands of intractable computation and artificial intelligence. Photonic computing is a competitive candidate, in light of the inherent properties of photons, including high propagation speed, strong robustness, and multiple degrees of freedom to encode information. Also, the progress of integrated photonics continues to provide novel possibilities, apart from boosting the scalability and stability of photonic computing architectures. Moreover, an introduction of quantum technology might open a new chapter for photonic computing, from the view of single photons. In this Perspective, we highlight the unique features and advances of integrated photonic platforms, whose roles in constructing a non-von Neumann computing architecture are also outlined. We show their potential in solving problems beyond the reach of traditional computers and in machine learning and further discuss the conceivable challenges and opportunities.

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Section: Introductionmentioning

confidence: 99%

Integrated Photonic Computing beyond the von Neumann Architecture

Jin

2023

ACS Photonics

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Photonic and optoelectronic neuromorphic computing

et al. 2022

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Recent advances in neuromorphic computing have established a computational framework that removes the processor-memory bottleneck evident in traditional von Neumann computing. Moreover, contemporary photonic circuits have addressed the limitations of electrical computational platforms to offer energy-efficient and parallel interconnects independently of the distance. When employed as synaptic interconnects with reconfigurable photonic elements, they can offer an analog platform capable of arbitrary linear matrix operations, including multiply–accumulate operation and convolution at extremely high speed and energy efficiency. Both all-optical and optoelectronic nonlinear transfer functions have been investigated for realizing neurons with photonic signals. A number of research efforts have reported orders of magnitude improvements estimated for computational throughput and energy efficiency. Compared to biological neural systems, achieving high scalability and density is challenging for such photonic neuromorphic systems. Recently developed tensor-train-decomposition methods and three-dimensional photonic integration technologies can potentially address both algorithmic and architectural scalability. This tutorial covers architectures, technologies, learning algorithms, and benchmarking for photonic and optoelectronic neuromorphic computers.

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Energy-Efficient Photonic Spiking Neural Network on a monolithic silicon CMOS photonic platform

Abstract: We designed, simulated, and taped-out a photonic spiking neural network on a monolithic silicon CMOS photonic platform. Benchmarking shows proposed PSNN outperforms other neuromorphic hardware with 21.09fJ/spike and 61.4 W average power at MNIST experiment.

Cited by 2 publications

References 27 publications

Integrated Photonic Computing beyond the von Neumann Architecture

Integrated Photonic Computing beyond the von Neumann Architecture

Photonic and optoelectronic neuromorphic computing

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