Single-pixel pattern recognition with coherent nonlinear optics

Bu, Ting; Kumar, S. Chaitanya; Zhang, He; Huang, Irwin; Huang, Yu‐Ping

doi:10.1364/ol.411564

Cited by 19 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yet, the achievable performance may differ with the dynamic range of the respective system output (e.g., bandwidth and spectral sensitivity to the input field may vary). Overall, frequency‐domain approaches involving second‐order [ 13,14,50 ] or third‐order nonlinearity (this work) seem to be a particularly promising degree of freedom regarding sub‐pJ energy consumption per inference. Nonlinear systems that operate on the Kerr effect (e.g., soliton fission, four‐wave mixing) would come with the additional benefit of frequency windows that are widely customizable for a wide range of optical amplifiers and cascaded operations.…”

Section: Discussionmentioning

confidence: 99%

“…For instance, emulating the firing of a neuron necessitates programmable nonlinear optical interconnects which enact synaptic activation functions. [ 13 , 14 , 15 ] These interconnects are power‐hungry and difficult to scale, [ 16 ] making the sequential arrangement of optical neural nodes, unlike biological neurons, potentially impractical.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neuromorphic Computing via Fission‐based Broadband Frequency Generation

Fischer,

Chemnitz,

Zhu

et al. 2023

Advanced Science

View full text Add to dashboard Cite

The performance limitations of traditional computer architectures have led to the rise of brain‐inspired hardware, with optical solutions gaining popularity due to the energy efficiency, high speed, and scalability of linear operations. However, the use of optics to emulate the synaptic activity of neurons has remained a challenge since the integration of nonlinear nodes is power‐hungry and, thus, hard to scale. Neuromorphic wave computing offers a new paradigm for energy‐efficient information processing, building upon transient and passively nonlinear interactions between optical modes in a waveguide. Here, an implementation of this concept is presented using broadband frequency conversion by coherent higher‐order soliton fission in a single‐mode fiber. It is shown that phase encoding on femtosecond pulses at the input, alongside frequency selection and weighting at the system output, makes transient spectro‐temporal system states interpretable and allows for the energy‐efficient emulation of various digital neural networks. The experiments in a compact, fully fiber‐integrated setup substantiate an anticipated enhancement in computational performance with increasing system nonlinearity. The findings suggest that broadband frequency generation, accessible on‐chip and in‐fiber with off‐the‐shelf components, may challenge the traditional approach to node‐based brain‐inspired hardware design, ultimately leading to energy‐efficient, scalable, and dependable computing with minimal optical hardware requirements.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Neuromorphic Computing via Fission‐based Broadband Frequency Generation

Fischer,

Chemnitz,

Zhu

et al. 2023

Advanced Science

View full text Add to dashboard Cite

show abstract

“…In addition, the operation complexity is also relatively low and for further integrations, the atomic vapor cell can also be replaced by semiconductor saturable absorbers (SESAM) [32]. Besides, [33][34][35] show that optical nonlinearities can also be used for mode filtering or data pre-processing, etc. aside from being used as nonlinear activation functions in neural networks.…”

Section: Discussionmentioning

confidence: 99%

Optical Convolutional Neural Network with Atomic Non-linearity

Yang

Robertson

Rodriguez

et al. 2021

2021 Conference on Lasers and Electro-Optics Europe &Amp; European Quantum Electronics Conference (CLEO/Europe-EQEC)

View full text Add to dashboard Cite

Due to their high degree of parallelism, fast processing speeds and low power consumption, analog optical functional elements offer interesting routes for realizing neuromorphic computer hardware. For instance, convolutional neural networks lend themselves to analog optical implementations by exploiting the Fourier-transform characteristics of suitable designed optical setups. However, the efficient implementation of optical nonlinearities for such neural networks still represents challenges. In this work, we report on the realization and characterization of a three-layer optical convolutional neural network where the linear part is based on a 4f-imaging system and the optical nonlinearity is realized via the absorption profile of a cesium atomic vapor cell. This system classifies the handwritten digital dataset MNIST with 83.96% accuracy, which agrees well with corresponding simulations. Our results thus demonstrate the viability of utilizing atomic nonlinearities in neural network architectures with low power consumption.

show abstract

“…Recently, there has been increasing interest on artificial neural networks using optics, leveraging its remarkable speed, multiplexing capability, and little heat deposition [10,11]. Usually, these optical neural networks are wholly trained with a known data set to optimize their connectivity and parameters through nonlinear layers [12,13]. However, such training is usually energy and time consuming, and its efficiency varies by the complexity of task, the size of the network, the nonlinearity and connectivity between the nodes.…”

Section: Introductionmentioning

confidence: 99%

Efficient Reservoir Computing using Field Programmable Gate Array and Electro-optic Modulation

Kumar

Jin

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

We experimentally demonstrate a hybrid reservoir computing system consisting of an electro-optic modulator and field programmable gate array (FPGA). It implements delay lines and filters digitally for flexible dynamics and high connectivity, while supporting a large number of reservoir nodes. To evaluate the system's performance and versatility, three benchmark tests are performed. The first is the 10 𝒕𝒉 order Nonlinear Auto-Regressive Moving Average test (NARMA-10), where the predictions of 1000 and 25,000 steps yield impressively low normalized root mean square errors (NRMSE's) of 0.142 and 0.148, respectively. Such accurate predictions over into the far future speak to its capability of large sample size processing, as enabled by the present hybrid design. The second is the Santa Fe laser data prediction, where a normalized mean square error (NMSE) of 6.73×10 −3 is demonstrated. The third is the isolate spoken digit recognition, with a word error rate close to 0.34%. Accurate, versatile, flexibly reconfigurable, and capable of long-term prediction, this reservoir computing system could find a wealth of impactful applications in real-time information processing, weather forecasting, and financial analysis.

show abstract

Single-pixel pattern recognition with coherent nonlinear optics

Cited by 19 publications

References 38 publications

Neuromorphic Computing via Fission‐based Broadband Frequency Generation

Neuromorphic Computing via Fission‐based Broadband Frequency Generation

Optical Convolutional Neural Network with Atomic Non-linearity

Efficient Reservoir Computing using Field Programmable Gate Array and Electro-optic Modulation

Contact Info

Product

Resources

About