Analog Optical Computers Accelerate AI Inference and Combinatorial Optimization for Sustainable, High-Speed Computing

Analog Optical Computer for AI Inference and Combinatorial Optimization

Artificial intelligence (AI) and combinatorial optimization are critical for many scientific and industrial applications. However, their growing energy demands challenge the sustainability of current digital computing systems. Most unconventional computing designs focus on either AI or optimization and rely heavily on energy-consuming digital conversions, which limit efficiency and scalability.

Introducing an analog optical computer (AOC) that merges analog electronics with three-dimensional optics to accelerate both AI inference and combinatorial optimization on one platform. This approach uses a rapid fixed-point search method that bypasses digital conversions, improving energy efficiency and noise robustness. The AOC supports compute-bound neural models with recursive reasoning and advanced gradient-descent optimization methods.

How the Analog Optical Computer Works

The AOC combines optical and analog electronic components within a feedback loop to perform fixed-point searches. Each iteration lasts about 20 nanoseconds. During this time, optics handle matrix–vector multiplications, while analog electronics perform nonlinear operations, subtraction, and annealing. This fixed-point design improves noise tolerance, essential for analog hardware.

The key hardware components include a microLED array, a spatial light modulator (SLM), and a photodetector array. The microLED array acts as the light source and encodes neural network activations or optimization variables. The SLM stores weights or coefficients and multiplies them with incoming light. Photodetectors then convert optical signals to analog electronic signals for further computation.

Addressing Challenges in AI and Optimization

The AOC deals with two main challenges seen in unconventional computing. First, hybrid systems often accelerate linear operations but fall back on digital nonlinearities, leading to energy-heavy conversions. The AOC removes this bottleneck by fully embracing analog processing. Second, it addresses the mismatch between hardware and applications—memory-heavy AI models and real-world optimization problems are hard to map efficiently on existing analog platforms.

By implementing a unified fixed-point abstraction, the AOC bridges this gap, enabling efficient processing for both AI inference and combinatorial optimization workloads.

Applications in Machine Learning

The AOC supports neural equilibrium models, which rely on iterative fixed-point updates to reach stable network outputs. These models have dynamic depth, allowing recursive reasoning and improved generalization, especially outside the training distribution. Such models are typically compute-heavy on digital chips but fit naturally within the AOC’s architecture.

Regression Case Study

Nonlinear regression demands continuous-valued outputs, challenging for analog systems due to noise. The AOC successfully handled regression tasks for Gaussian and sinusoidal functions, reproducing these curves with good accuracy. This shows the analog hardware’s ability to run equilibrium models effectively despite inherent noise.

Classification Case Study

For datasets like MNIST and Fashion-MNIST, the AOC’s predicted labels matched digital results for 99.8% of inputs. This validates the approach of training models digitally and then transferring weights for analog opto-electronic inference. The equilibrium model’s benefits become clear when compared with simpler linear classifiers.

Optimization Capabilities

The AOC supports quadratic unconstrained mixed optimization (QUMO), a flexible framework that handles binary and continuous variables, covering a broad range of real-world combinatorial problems. One example is compressed sensing, where the AOC reconstructs signals from fewer measurements than typical methods. It also finds globally optimal solutions in transaction settlement problems through block coordinate descent steps.

Benchmarking Results

Extensive testing on synthetic QUMO and QUBO problems shows the AOC achieves over 95% proximity to optimal objectives for QUMO and 100% for QUBO within 1,000 samples. This demonstrates strong performance across diverse, challenging optimization tasks.

Looking Ahead: Scalability and Efficiency

Scaling the AOC from thousands to billions of weights is necessary for practical AI and optimization applications. Its modular design can break down large matrix–vector multiplications into smaller subproblems, making scalability feasible. Early projections suggest energy efficiency around 500 tera-operations per second per watt at 8-bit precision, a substantial improvement over traditional digital systems.

The AOC’s fixed-point search approach and fully analog design promise a path to more efficient, faster AI inference and combinatorial optimization. This co-design of hardware and algorithms could drive future innovations essential for sustainable computing.

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