Demonstration of scalable microring weight bank control for large-scale photonic integrated circuits

Huang, Chaoran; Bilodeau, Simon; Lima, Thomas Ferreira de; Tait, Alexander N.; Philip, Y.; Blow, Eric C.; Jha, Aashu; Peng, Hsuan-Tung; Shastri, Bhavin J.; Prucnal, Paul R.

doi:10.1063/1.5144121

Cited by 81 publications

(31 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In particular, they show that the PEMAN can trade off not only speed with resolution, but also power consumption with resolution. Moreover, the found ENOB are in line with the performance of similar devices found in the recent literature [59,60].…”

Section: B Peman Validation With Random-valued Multiplicationssupporting

confidence: 91%

A Codesigned Integrated Photonic Electronic Neuron

Marinis¹,

Catania²,

Castoldi³

et al. 2022

Preprint

View full text Add to dashboard Cite

In the modern era of artificial intelligence, increasingly sophisticated artificial neural networks (ANNs) are implemented, which pose challenges in terms of execution speed and power consumption. To tackle this problem, recent research on reduced-precision ANNs opened the possibility to exploit analog hardware for neuromorphic acceleration. In this scenario, photonic-electronic engines are emerging as a short-medium term solution to exploit the high speed and inherent parallelism of optics for linear computations needed in ANN, while resorting to electronic circuitry for signal conditioning and memory storage. In this paper we introduce a precision-scalable integrated photonic-electronic multiply-accumulate neuron, namely PEMAN. The proposed device relies on (i) an analog photonic engine to perform reduced-precision multiplications at high speed and low power, and (ii) an electronic front-end for accumulation and application of the nonlinear activation function by means of a nonlinear encoding in the analog-to-digital converter (ADC). The device, based on the iSiPP50G SOI process for the photonic engine and a commercial 28 nm CMOS process for the electronic front end, has been numerically validated through cosimulations to perform multiply-accumulate operations (MAC). PEMAN exhibits a multiplication accuracy of 6.1 ENOB up to 10 GMAC/s, while it can perform computations up to 56 GMAC/s with a reduced accuracy down to 2.1 ENOB. The device can trade off speed with resolution and power consumption, it outperforms its analog electronics counterparts both in terms of speed and power consumption, and brings substantial improvements also compared to a leading GPU.

show abstract

Section: B Peman Validation With Random-valued Multiplicationssupporting

confidence: 91%

A Codesigned Integrated Photonic Electronic Neuron

Marinis¹,

Catania²,

Castoldi³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Different methods of controlling large-scale MRRs for matrix computation were proposed and demonstrated in refs. 85 , 113 , 114 . Afterwards, the microring weight bank was applied for various signal processing methods, such as fiber nonlinearity compensation 115 and photonic PCA 87 .…”

Section: Mvms For Optical Signal Processingmentioning

confidence: 99%

Photonic matrix multiplication lights up photonic accelerator and beyond

et al. 2022

Self Cite

View full text Add to dashboard Cite

Matrix computation, as a fundamental building block of information processing in science and technology, contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms. Photonic accelerators are designed to accelerate specific categories of computing in the optical domain, especially matrix multiplication, to address the growing demand for computing resources and capacity. Photonic matrix multiplication has much potential to expand the domain of telecommunication, and artificial intelligence benefiting from its superior performance. Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors. In this review, we first introduce the methods of photonic matrix multiplication, mainly including the plane light conversion method, Mach–Zehnder interferometer method and wavelength division multiplexing method. We also summarize the developmental milestones of photonic matrix multiplication and the related applications. Then, we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years. Finally, we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.

show abstract

“…In the feedback control rule shown in Figure 3d, the controller performs a binary search algorism for a target transmission value, and the commanded value for the thermoelectric control step is determined by the converged value of

δ .

Utilizing this feedback control procedure, two‐channel photonic principal component analysis (PCA) and independent component analysis (ICA) have been experimentally demonstrated with on‐chip MRR weight banks, which can extract the principal components and recover the independent components solely based on the statistical information of the weighted addition output. [ 75,76 ] Furthermore, by simplifying the transmission edge calibration procedure, a record high accuracy of 6.6 dB and precision of 6.5 dB with negligible interchannel crosstalk are achieved for the four‐channel weight bank control, [ 77 ] significantly outperforming the digital weight resolution (5 bits) used in the TrueNorth architecture. [ 25 ] Apart from the stable control of the MRR weight bank, channel density is another important factor for large‐scale networks.…”

Section: Photonic Integrated Synapsesmentioning

confidence: 99%

Integrated Neuromorphic Photonics: Synapses, Neurons, and Neural Networks

Guo

Xiang

Zhang

et al. 2021

Advanced Photonics Research

View full text Add to dashboard Cite

Ever‐growing demands of bandwidth, computing speed, and power consumption are now accelerating the transformation of computing research, as work‐at‐home becomes a new normal. Brain‐inspired photonic neuromorphic computing for artificial intelligence is raising an urgent need, and it promises orders‐of‐magnitude higher computing speed and energy efficiency compared with digital electronic counterparts. Photonic neuromorphic networks combine the efficiency of neural networks based on a non‐von Neumann architecture and the benefits of photonics to constitute a new computing paradigm. Herein, some recent advances in photonic neural networks are reviewed, including the concept, principle, key photonic components, and architectures that construct the neuromorphic systems, hoping to provide a better understanding of this emerging field.

show abstract

Demonstration of scalable microring weight bank control for large-scale photonic integrated circuits

Cited by 81 publications

References 32 publications

A Codesigned Integrated Photonic Electronic Neuron

A Codesigned Integrated Photonic Electronic Neuron

Photonic matrix multiplication lights up photonic accelerator and beyond

Integrated Neuromorphic Photonics: Synapses, Neurons, and Neural Networks

Contact Info

Product

Resources

About