Ultra-compact multi-task processor based on in-memory optical computing.

ABSTRACT: To enhance the computational density and energy efficiency of on-chip neuromorphic hardware, this study introduces a novel network architecture for multi-task processing with in-memory optical computing. On-chip optical neural networks are celebrated for their capability to transduce a substantial volume of parameters into optical form while conducting passive computing, yet they encounter challenges in scalability and multitasking. Leveraging the principles of transfer learning, this approach involves embedding the majority of parameters into fixed optical components and a minority into adjustable electrical components. Furthermore, with deep regression algorithm in modeling physical propagation process, a compact optical neural network achieve to handle diverse tasks. In this work, two u

SUBMITTER: Liu W 

PROVIDER: S-EPMC11930997 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature