32-bit photonic processor beyond noise limitation based on parallelized bit-slicing

Photonic processors have emerged as powerful platforms for large-scale signal processing, offering distinct benefits over traditional electronic computing, such as large bandwidth, superior parallelism, and energy efficiency. However, their practical deployment is limited by inadequate computational precision owing to device insertion loss, link noise, and uneven chip fabrication. This study introduces a 32-bit photonic processor that overcomes the limitations of conventional photonic processors. The proposed approach employs the parallelized bit-slicing principle to decompose high-precision operations into multiple low-precision operations, thereby enabling high-precision computing using low-precision analog photonic devices. This study demonstrates 32-bit photonic computing and yields substantial performance improvements over conventional approaches in computing tasks such as image segmentation and recognition. This breakthrough mitigates the impact of nonideal conditions on computational precision and facilitates the practical deployment of photonic processors in artificial intelligence-driven and large-scale data processing scenarios.