Home Community This Research Explains How Simplified Optical Neural Network Component Saves Space And Energy

This Research Explains How Simplified Optical Neural Network Component Saves Space And Energy

This Research Explains How Simplified Optical Neural Network Component Saves Space And Energy

In optical computing, a pressing challenge is the efficient implementation of real-valued optical matrix-vector multiplication (MVM). While optical computing offers benefits reminiscent of high bandwidth, low latency, and energy efficiency, traditional optical matrix computing methods have been designed for complex-valued matrices, leading to a big redundancy of resources when coping with real-valued matrices. This redundancy consumes extra energy and results in an expanded chip footprint, raising concerns about space efficiency and scalability in large-scale optical neural networks (ONNs) and optimization problem solvers.

Efforts to handle this issue have been made, with solutions reminiscent of a pseudo-real-value MZI mesh. The pseudo-real-value MZI mesh aimed to scale back the variety of phase shifters required for real-valued matrices but introduced complexities related to coherent detection and extra reference paths, potentially introducing sources of error and layout intricacies.

In response to those challenges, a novel and simplified solution has emerged as a Real-Valued MZI Mesh for incoherent optical MVM. This revolutionary approach reduces the dimensions of phase shifters required to N^2 while maintaining an optical depth of N + 1. As an alternative of detecting the complex value of the output optical field, this method employs an additional port to perform optical power subtraction, yielding a real-valued output. This not only streamlines the hardware requirements but additionally simplifies the detection process, overcoming the restrictions of previous solutions.

To evaluate the performance and viability of the proposed Real-Valued MZI Mesh, extensive numerical evaluations were conducted utilizing particle swarm optimization (PSO). The outcomes of those evaluations demonstrated the mesh’s exceptional performance in benchmark tasks, highlighting its potential as an efficient solution for real-valued optical MVM in ONNs. Moreover, error analyses revealed its resilience to fabrication errors, enhancing its reliability for practical applications.

Moreover, the study introduced a matched on-chip nonlinear activation function, further emphasizing the mesh’s suitability for large-scale ONNs. With its space efficiency, energy efficiency, scalability, and robustness to fabrication errors, the Real-Valued MZI Mesh emerges as a promising solution to all of the challenges posed by real-valued optical matrix computing. As the sphere of optical computing continues to evolve, this revolutionary approach holds significant promise for the long run of large-scale ONNs and combination optimization problem solvers, offering a more efficient and practical path forward.

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Niharika is a Technical consulting intern at Marktechpost. She is a 3rd 12 months undergraduate, currently pursuing her B.Tech from Indian Institute of Technology(IIT), Kharagpur. She is a highly enthusiastic individual with a keen interest in Machine learning, Data science and AI and an avid reader of the most recent developments in these fields.

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