A compression-decompression framework for universal acceleration of photonic neuromorphic computing

Photonic neuromorphic computing provides an effective route to overcoming the fundamental bottlenecks of the von Neumann architecture. However, the realization of complex optical nonlinearities by time-division multiplexing in traditional photonic neuromorphic computing limits both information-processing speed and scalability of these systems. Here, we break this bottleneck by introducing an accuracy-compensated compression strategy that co-optimizes latency and speed without sacrificing computational fidelity. Our core innovation is an eigenvector-driven compression-decompression framework that projects input data into a task-relevant subspace, drastically reducing the data volume processed by the hidden layer. This paradigm shift enables photonic neuromorphic computing to achieve image recognition and time-series prediction tasks in the optical domain at speeds of 100 million images and 600 million data points per second, respectively. We experimentally validate our approach across both a continuous-wave system based on off-the-shelf lasers and a spiking system composed of a specifically fabricated photonic neuro-synaptic chip, demonstrating that the compression-decompression framework effectively recovers the accuracy to the level of uncompressed networks—e.g., achieving >95% on MNIST and >84% on Fashion-MNIST—while slashing latency and hardware resource demands by an order of magnitude. This strategy establishes a scalable pathway towards high-throughput, low-power neuromorphic chips, bridging a critical gap between algorithmic efficiency and physical computing paradigms.