Balanced WDM weight banks for analog optical processing and networking in silicon

Tait, Alexander N.; Nahmias, Mitchell A.; Shastri, Bhavin J.; Chang, Matthew P.; Wu, Allie X.; Zhou, Ellen; Blow, Eric C.; Lima, Thomas Ferreira de; Wu, Ben; Prucnal, Paul R.

doi:10.1109/phosst.2015.7248172

Cited by 3 publications

(5 citation statements)

References 3 publications

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“…The two optical paths of silicon weight banks must be matched in amplitude, delay, and phase. [ 25 ] If mismatched, there will be a limitation to the accuracy of the weighting achieved by subtraction as shown in Equation (). The amplitude mismatch term, Δα, is the primary mechanism used to generate the weight, w i .…”

Section: Weighting Accuracy At High Operating Frequenciesmentioning

confidence: 99%

Radio‐Frequency Linear Analysis and Optimization of Silicon Photonic Neural Networks

Blow,

Bilodeau,

Zhang

et al. 2024

Advanced Photonics Research

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Broadband analog signal processors utilizing silicon photonics have demonstrated a significant impact in numerous application spaces, offering unprecedented bandwidths, dynamic range, and tunability. In the past decade, microwave photonic techniques have been applied to neuromorphic processing, resulting in the development of novel photonic neural network architectures. Neuromorphic photonic systems can enable machine learning capabilities at extreme bandwidths and speeds. Herein, low‐quality factor microring resonators are implemented to demonstrate broadband optical weighting. In addition, silicon photonic neural network architectures are critically evaluated, simulated, and optimized from a radio‐frequency performance perspective. This analysis highlights the linear front‐end of the photonic neural network, the effects of linear and nonlinear loss within silicon waveguides, and the impact of electrical preamplification.

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Section: Weighting Accuracy At High Operating Frequenciesmentioning

confidence: 99%

Radio‐Frequency Linear Analysis and Optimization of Silicon Photonic Neural Networks

Blow,

Bilodeau,

Zhang

et al. 2024

Advanced Photonics Research

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“…Their analog nature allows for signal processing in continuous time, and reduces the cost, memory requirements, and precision loss resulting from the need to digitize massive amounts of data. Not only are these advantages for applications involving optical signals, such as optical fiber or free space optical communication [2][3][4][5][6][7][8][9][10][11][12][13] , but they also show promise when applied to computation in general [14][15][16][17][18][19][20][21][22][23][24] .…”

Section: Introductionmentioning

confidence: 99%

“…Photonic neuromorphic computing is a field that aims to leverage the advantages of analog photonic circuits for highperformance computing tasks. In particular, it leverages the high speed, wavelength-division parallelism, and low power consumption of photonic arithmetic operations to accelerate the calculations that make up artificial neural networks [14,[16][17][18][19][20][21][22][23][24]27,28,[34][35][36][37] . This enables simple yet powerful networks to solve complex tasks at the speed light transmission [13] .…”

Section: Introductionmentioning

confidence: 99%

“…[14], where it used photonic spike processing to perform the operations of a spiking neural network. Since then, multiple techniques have been made to improve upon this design, including tunable weight banks made from interferometers [14,35] , tunable resonators [15][16][17] , or phasechange elements [22][23][24]34] , as well as a variety of output designs including all-optical thresholders [22,23] , electro-optical nonlinear laser gates [28,36] , and electrical sampling and reproduction circuitry [18,19,24] . Today, it is showing particular promise in the fields of high speed neural signal processing [13,37] and machine vision acceleration [19][20][21][22][23][24] .…”

Section: Introductionmentioning

confidence: 99%

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Photonic analog signal processing and neuromorphic computing [Invited]

Garofolo,

2024

中国光学快报

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In this review paper, we discuss the properties and applications of photonic computing and analog signal processing. Photonic computational circuits have large operation bandwidth, low power consumption, and fine frequency control, enabling a wide range of application-specific computational techniques that are impossible to implement using traditional electrical and digital hardware alone. These advantages are illustrated in the elegant implementation of optical steganography, the real-time blind separation of signals in the same bandwidth, and the efficient acceleration of artificial neural network inference. The working principles and use of photonic circuits for analog signal processing and neuromorphic computing are reviewed and notable demonstrated applications are highlighted.

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“…Prior work demonstrated feedforward control of an add/drop MRR filter edge for effecting a continuous range of transmission values. This enabled a single photonic weight with a range of -1 to +1 [27] and precision of 3.1 bits [26] (i.e. a maximum error of ±0.117 over the range ±1, or a dynamic range of 9.33dB).…”

Section: Introductionmentioning

confidence: 99%

Multi-channel control for microring weight banks

et al. 2016

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We demonstrate 4-channel, 2GHz weighted addition in a silicon microring filter bank. Accurate analog weight control becomes more difficult with increasing number of channels, N, as feedback approaches become impractical and brute force feedforward approaches take O(2N) calibration measurements in the presence of inter-channel dependence. We introduce model-based calibration techniques for thermal cross-talk and cross-gain saturation, which result in a scalable O(N) calibration routine and 3.8 bit feedforward weight accuracy on every channel. Practical calibration routines are indispensible for controlling large-scale microring systems. The effect of thermal model complexity on accuracy is discussed. Weighted addition based on silicon microrings can apply the strengths of photonic manufacturing, wideband information processing, and multiwavelength networks towards new paradigms of ultrafast analog distributed processing.

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Balanced WDM weight banks for analog optical processing and networking in silicon

Cited by 3 publications

References 3 publications

Radio‐Frequency Linear Analysis and Optimization of Silicon Photonic Neural Networks

Radio‐Frequency Linear Analysis and Optimization of Silicon Photonic Neural Networks

Photonic analog signal processing and neuromorphic computing [Invited]

Multi-channel control for microring weight banks

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