Acceleration Method for Learning Fine-Layered Optical Neural Networks
An optical neural network (ONN) is a promising system due to its high-speed and low-power operation. Its linear unit performs a multiplication of an input vector and a weight matrix in optical analog circuits. Among them, a circuit with a multiple-layered structure of programmable Mach-Zehnder interferometers (MZIs) can realize a specific class of unitary matrices with a limited number of MZIs as its weight matrix. The circuit is effective for balancing the number of programmable MZIs and ONN performance. However, it takes a lot of time to learn MZI parameters of the circuit with a conventional automatic differentiation (AD), which machine learning platforms are equipped with. To solve the time-consuming problem, we propose an acceleration method for learning MZI parameters. We create customized complex-valued derivatives for an MZI, exploiting Wirtinger derivatives and a chain rule. They are incorporated into our newly developed function module implemented in C++ to collectively calculate their values in a multi-layered structure. Our method is simple, fast, and versatile as well as compatible with the conventional AD. We demonstrate that our method works 20 times faster than the conventional AD when a pixel-by-pixel MNIST task is performed in a complex-valued recurrent neural network with an MZI-based hidden unit.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
LayeredFlow: A Real-World Benchmark for Non-Lambertian Multi-Layer Optical Flow
Achieving 3D understanding of non-Lambertian objects is an important task with many useful applications, but most existing algorithms struggle to deal with such objects. One major obstacle towards progress in this field …
DiversityOptical Flow EstimationLayered Optical Flow Estimation Using a Deep Neural Network with a Soft Mask
Using a layered representation for motion estimation has the advantage of being able to cope with discontinuities and occlusions. In this paper, we learn to estimate optical flow by combining a layered motion representat…
Motion EstimationOptical Flow EstimationLayered image motion with explicit occlusions, temporal consistency, and depth ordering
Layered models are a powerful way of describing natural scenes containing smooth surfaces that may overlap and occlude each other. For image motion estimation, such models have a long history but have not achieved the wi…
Motion EstimationOptical Flow EstimationScene SegmentationFlowIt: Global Matching via Hierarchical Transformers and Optimal Transport for Optical Flow
We present FlowIt, a novel architecture for optical flow estimation that combines global matching with confidence and occlusion-guided refinement. At its core, FlowIt leverages a hierarchical transformer architecture tha…
Zero-shot GeneralizationOptical Flow EstimationOptical Computing for Deep Neural Network Acceleration: Foundations, Recent Developments, and Emerging Directions
Emerging artificial intelligence applications across the domains of computer vision, natural language processing, graph processing, and sequence prediction increasingly rely on deep neural networks (DNNs). These DNNs req…