The physics of optical computing
arXiv:2308.00088 · doi:10.1038/s42254-023-00645-5
Abstract
There has been a resurgence of interest in optical computing over the past decade, both in academia and in industry, with much of the excitement centered around special-purpose optical computers for neural-network processing. Optical computing has been a topic of periodic study for over 50 years, including for neural networks three decades ago, and a wide variety of optical-computing schemes and architectures have been proposed. In this paper we provide a systematic explanation of why and how optics might be able to give speed or energy-efficiency benefits over electronics for computing, enumerating 11 features of optics that can be harnessed when designing an optical computer. One often-mentioned motivation for optical computing -- that the speed of light is fast -- is not a key differentiating physical property of optics for computing; understanding where an advantage could come from is more subtle. We discuss how gaining an advantage over state-of-the-art electronic processors will likely only be achievable by careful design that harnesses more than one of the 11 features, while avoiding a number of pitfalls that we describe.
arXiv comment31 pages; 11 figures
References in corpus (38)
- Deep Learning with Coherent Nanophotonic Circuits
- Parallel convolution processing using an integrated photonic tensor core
- Photonics for artificial intelligence and neuromorphic computing
- 11 TeraFLOPs per second photonic convolutional accelerator for deep learning optical neural networks
- Microresonator solitons for massively parallel coherent optical communications
- Universal Linear Optics
- Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems
- Single-chip photonic deep neural network for instantaneous image classification
- Attojoule Optoelectronics for Low-Energy Information Processing and Communications: a Tutorial Review
- Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit
- Synthetic three-dimensional atomic structures assembled atom by atom
- Network of Time-Multiplexed Optical Parametric Oscillators as a Coherent Ising Machine
- 30 years of squeezed light generation
- Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks
- Ultra-low loss integrated visible photonics using thin-film lithium niobate
- An optical neural network using less than 1 photon per multiplication
- Image sensing with multilayer, nonlinear optical neural networks
- Superconducting optoelectronic circuits for neuromorphic computing
- Delocalized Photonic Deep Learning on the Internet's Edge
- Scalable Optical Learning Operator
- Prospects and applications of photonic neural networks
- Reduced Material Loss in Thin-film Lithium Niobate Waveguides
- Femtojoule, femtosecond all-optical switching in lithium niobate nanophotonics
- Three dimensional waveguide-interconnects for scalable integration of photonic neural networks
- Random Projections through multiple optical scattering: Approximating kernels at the speed of light
- A full degree-of-freedom photonic crystal spatial light modulator
- An Optical Frontend for a Convolutional Neural Network
- Design and analysis of photonic crystal coupled cavity arrays for quantum simulation
- Asymptotically Fault-Tolerant Programmable Photonics
- Understanding and mitigating noise in trained deep neural networks
- Quantifying power use in silicon photonic neural networks
- Remnants of semiclassical bistability in the few-photon regime of cavity QED
- Nonlinear interferometry approach to photonic sequential logic
- Macroscopic electron quantum coherence in a solid-state circuit
- Optical Neural Networks: The 3D connection
- Thermalization of strongly interacting bosons after spontaneous emissions in optical lattices
- Photonic unsupervised learning processor for secure and high-throughput optical fiber communication
- On the physical (im)possibility of lightsabers
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- Programmable metasurfaces for future photonic artificial intelligence
- Transferable polychromatic optical encoder for neural networks
- Waveguide-multiplexed photonic matrix-vector multiplication processor using multiport photodetectors
- Asymmetrical estimator for training encapsulated deep photonic neural networks
- XLuminA: An Auto-differentiating Discovery Framework for Super-Resolution Microscopy
- Programmable 200 GOPS Hopfield-inspired photonic Ising machine
- Dawn and fall of non-Gaussianity in the quantum parametric oscillator
- Meta-operators for all-optical image processing
- Multi-task Photonic Reservoir Computing: Wavelength Division Multiplexing for Parallel Computing with a Silicon Microring Resonator
- Arbitrary control over multimode wave propagation for machine learning
- Large-scale free-space photonic circuits in two dimensions
- Fault-Tolerant Neural Networks from Biological Error Correction Codes
- Programmable Space-Frequency Linear Transformations in Photonic Interlacing Architectures
- An All-Optical General-Purpose CPU and Optical Computer Architecture
- Coherence Awareness in Diffractive Neural Networks
- Photonic systolic array for all-optical matrix-matrix multiplication
- Quantum optical classifier with superexponential speedup
- Mechanical in-sensor computing: a programmable meta-sensor for structural damage classification without external electronic power
- Nonlinear unitary circuits for photonic neural networks
- Passive All-Optical Nonlinear Neuron Activation via PPLN Nanophotonic Waveguides
- Quantum-secure multiparty deep learning
- Spatial Optical Simulator for Classical Statistical Models
- Dense Associative Memory in a Nonlinear Optical Hopfield Neural Network
- Optical Algorithm for Derivative of Real-Valued Functions
- Neural networks with quantum states of light
- Shannon Entropy Helps Optimize the Performance of a Frequency-Multiplexed Extreme Learning Machine
- Polaritonic Machine Learning for Graph-based Data Analysis
- Scaling Analog Photonic Accelerators for Byte-Size, Integer General Matrix Multiply (GEMM) Kernels
- Integrated Photonic Devices in Thin-Film Barium Titanate: Opportunities and Challenges
- Implementation of transformer-based LLMs with large-scale optoelectronic neurons on a CMOS compatible platform
- Electrically driven plasmon-polaritonic bistability in Dirac electron tunneling transistors
- A ghost mechanism: An analytical model of abrupt learning in recurrent networks
- Switching, Amplifying, and Chirping Diode Lasers with Current Pulses for High Bandwidth Quantum Technologies