Optical neural networks (ONNs) are promising hardware platforms for next-generation artificial intelligence acceleration with ultrafast speed and low-energy consumption. However, previous ONN designs are bounded by one multiply–accumulate operation per device, showing unsatisfying scalability. In this work, we propose a scalable ONN architecture, dubbedSqueezeLight. We propose a nonlinear optical neuron based on multioperand ring resonators (MORRs) to squeeze vector dot-product into a single device with low wavelength usage and built-in nonlinearity. A block-level squeezing technique with structured sparsity is exploited to support higher scalability. We adopt a robustness-aware training algorithm to guarantee variation tolerance. To enable a truly scalable ONN architecture, we extendSqueezeLightto a separable optical CNN architecture that further squeezes in the layer level. Two orthogonal convolutional layers are mapped to one MORR array, leading to order-of-magnitude higher software training scalability. We further explore augmented representability forSqueezeLightby introducing parametric MORR neurons with trainable nonlinearity, together with a nonlinearity-aware initialization method to stabilize convergence. Experimental results show thatSqueezeLightachieves one-order-of-magnitude better compactness and efficiency than previous designs with high fidelity, trainability, and robustness. Our open-source codes are available athttps://github.com/JeremieMelo/SqueezeLight.

Optical neural networks (ONNs) have demonstrated record-breaking potential in high-performance neuromorphic computing due to their ultra-high execution speed and low energy consumption. However, current learning protocols fail to provide scalable and efficient solutions to photonic circuit optimization in practical applications. In this work, we propose a novel on-chip learning framework to release the full potential of ONNs for power-efficient in situ training. Instead of deploying implementation-costly back-propagation, we directly optimize the device configurations with computation budgets and power constraints. We are the first to model the ONN on-chip learning as a resource-constrained stochastic noisy zeroth-order optimization problem, and propose a novel mixed-training strategy with two-level sparsity and power-aware dynamic pruning to offer a scalable on-chip training solution in practical ONN deployment. Compared with previous methods, we are the first to optimize over 2,500 optical components on chip. We can achieve much better optimization stability, 3.7x-7.6x higher efficiency, and save >90% power under practical device variations and thermal crosstalk.

Optical neural networks (ONNs) have demonstrated promising potentials for next-generation artificial intelligence acceleration with ultra-low latency, high bandwidth, and low energy consumption. However, due to high area cost and lack of efficient sparsity exploitation, previous ONN designs fail to provide scalable and efficient neuromorphic computing, which hinders the practical implementation of photonic neural accelerators. In this work, we propose a novel design methodology to enable a more scalable ONN architecture. We propose a nonlinear optical neuron based on multi-operand ring resonators to achieve neuromorphic computing with a compact footprint, low wavelength usage, learnable neuron balancing, and built-in nonlinearity. The structured sparsity is exploited to support more efficient ONN engines via a fine-grained structured pruning technique. A robustness-aware learning method is adopted to guarantee the variation-tolerance of our ONN. Simulation and experimental results show that the proposed ONN achieves one-order-of-magnitude improvement in compactness and efficiency over previous designs with high fidelity and robustness.

Optical neuromorphic computing has demonstrated promising performance with ultra-high computation speed, high bandwidth, and low energy consumption. The traditional optical neural network (ONN) architectures realize neuromorphic computing via electrical weight encoding. However, previous ONN design methodologies can only handle static linear projection with stationary synaptic weights, thus fail to support efficient and flexible computing when both operands are dynamically-encoded light signals. In this work, we propose a novel ONN engine O2NN based on wavelength-division multiplexing and differential detection to enable high-performance, robust, and versatile photonic neural computing with both light operands. Balanced optical weights and augmented quantization are introduced to enhance the representability and efficiency of our architecture. Static and dynamic variations are discussed in detail with a knowledge-distillation-based solution given for robustness improvement. Discussions on hardware cost and efficiency are provided for a comprehensive comparison with prior work. Simulation and experimental results show that the proposed ONN architecture provides flexible, efficient, and robust support for high-performance photonic neural computing with fully-optical operands under low-bit quantization and practical variations.

As a promising neuromorphic framework, the optical neural network (ONN) demonstrates ultrahigh inference speed with low energy consumption. However, the previous ONN architectures have high area overhead which limits their practicality. In this article, we propose an area-efficient ONN architecture based on structured neural networks, leveraging optical fast Fourier transform for efficient computation. A two-phase software training flow with structured pruning is proposed to further reduce the optical component utilization. Experimental results demonstrate that the proposed architecture can achieve 2.2- 3.7× area cost improvement compared with the previous singular value decomposition-based architecture with comparable inference accuracy. A novel optical microdisk-based convolutional neural network architecture with joint learnability is proposed as an extension to move beyond Fourier transform and multilayer perception, enabling hardware-aware ONN design space exploration with lower area cost, higher power efficiency, and better noise-robustness.

Optical neural network (ONN) is a neuromorphic computing hardware based on optical components. Since its first on-chip experimental demonstration, it has attracted more and more research interests due to the advantages of ultra-high speed inference with low power consumption. In this work, we design a novel slimmed architecture for realizing optical neural network considering both its software and hardware implementations. Different from the originally proposed ONN architecture based on singular value decomposition which results in two implementation-expensive unitary matrices, we show a more area-efficient architecture which uses a sparse tree network block, a single unitary block and a diagonal block for each neural network layer. In the experiments, we demonstrate that by leveraging the training engine, we are able to find a comparable accuracy to that of the previous architecture, which brings about the flexibility of using the slimmed implementation. The area cost in terms of the Mach-Zehnder interferometers, the core optical components of ONN, is 15%-38% less for various sizes of optical neural networks.

Photonic integrated circuit (PIC), as a promising alternative to traditional CMOS circuit, has demonstrated the potential to accomplish on-chip optical interconnects and computations in ultra-high speed and/or low power consumption. Wavelength division multiplexing (WDM) is widely used in optical communication for enabling multiple signals being processed and transferred independently. In this work, we apply WDM to optical logic PIC synthesis to reduce the PIC area.

Photonic integrated circuit (PIC), as a promising alternative to traditional CMOS circuit, has demonstrated the potential to accomplish on-chip optical signal transmission and computations in ultra-high speed and/or low power consumption. One of the critical challenges of PIC, however, is that its scalability and robustness are limited by cascaded optical power loss and noise error. In this paper, we analyze the scalability and noise robustness challenges facing photonic integrated circuits, for two representative PIC applications: logic computing and neural networks. Automated design algorithms and learning methodologies are proposed to resolve these issues.

The development of photonic integrated circuits (PICs) has made it possible to accomplish on-chip optical interconnects and computations. As a promising alternative to traditional CMOS circuits, optics has demonstrated the ability to realize ultra-high speed and low-power information processing and communications. In this work, we propose a logic synthesis methodology for PICs. For the first time, practical issues including the insertion losses from optical combiners and switches are considered. Two optimization techniques based on binary decision diagram, combiner elimination and coupler assignment, are proposed to improve the power efficiency for PICs. Experimental results of MCNC and IWLS combinational benchmarks showed our method could efficiently generate quality PICs with a 27.02X better optical power efficiency on average, and greatly reduce the optical power depletion and facilitate large-scale on-chip optical computation.

As VLSI technology scales to deep sub-micron, optical interconnect becomes an attractive alternative for on-chip communication. The traditional optical routing works mainly optimize the path loss, and few works explicitly exploit the optical-electrical co-design of on-chip interconnects. To overcome these limitations, we present an efficient framework that directs the hybrid optical and electrical routes with a global view of power optimization. In this framework, on-chip signal bits are processed as hyper nets; the combination of optical and electrical routes are designed for hyper nets; then a formulation is given to find the appropriate solution of each hyper net and follows a speed-up algorithm; a min-cost max-flow network is utilized to reduce the consumed optical waveguides. Experimental results demonstrate the effectiveness of the proposed framework.