2023/10/17 by Mostafa Honari Latifpour, Byoung Jun Park, Latifpour, Mostafa Honari +5 · 1 citation
Computer Science · Engineering · #Advanced Memory and Neural Computing #Applied Physics (physics.app-ph) #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Neural Networks and Reservoir Computing #Neural and Evolutionary Computing (cs.NE) #Optics (physics.optics) #Photonic and Optical Devices
paper · pdf · doi:10.48550/arxiv.2310.11014
openalex publication_date 2023/10/17 · openalex created_date 2023/10/21 · openalex updated_date 2026/07/28
The rapid advancements in machine learning across numerous industries have amplified the demand for extensive matrix-vector multiplication operations, thereby challenging the capacities of traditional von Neumann computing architectures. To address this, researchers are currently exploring alternatives such as in-memory computing systems to develop faster and more energy-efficient hardware. In particular, there is renewed interest in computing systems based on optics, which could potentially handle matrix-vector multiplication in a more energy-efficient way. Despite promising initial results, developing a highly parallel, programmable, and scalable optical computing system capable of rivaling electronic computing hardware still remains elusive. In this context, we propose a hyperspectral in-memory computing architecture that integrates space multiplexing with frequency multiplexing of optical frequency combs and uses spatial light modulators as a programmable optical memory, thereby boosting the computational throughput and the energy efficiency. We have experimentally demonstrated multiply-accumulate operations with higher than 4-bit precision in both matrix-vector and matrix-matrix multiplications, which suggests the system's potential for a wide variety of deep learning and optimization tasks. This system exhibits extraordinary modularity, scalability, and programmability, effectively transcending the traditional limitations of optics-based computing architectures. Our approach demonstrates the potential to scale beyond peta operations per second, marking a significant step towards achieving high-throughput energy-efficient optical computing.