Sophisticated deep learning with on-chip optical diffractive tensor processing

• URL: http://arxiv.org/abs/2212.09975v1
• Date: Tue, 20 Dec 2022 03:33:26 GMT
• Title: Sophisticated deep learning with on-chip optical diffractive tensor processing
• Authors: Yuyao Huang, Tingzhao Fu, Honghao Huang, Sigang Yang, Hongwei Chen
• Abstract summary: Photonic integrated circuits provide an efficient approach to mitigate bandwidth limitations and power-wall brought by electronic counterparts. We propose an optical computing architecture enabled by on-chip diffraction to implement convolutional acceleration, termed optical convolution unit (OCU) With OCU as the fundamental unit, we build an optical convolutional neural network (oCNN) to implement two popular deep learning tasks: classification and regression.
• Score: 5.081061839052458
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ever-growing deep learning technologies are making revolutionary changes for modern life. However, conventional computing architectures are designed to process sequential and digital programs, being extremely burdened with performing massive parallel and adaptive deep learning applications. Photonic integrated circuits provide an efficient approach to mitigate bandwidth limitations and power-wall brought by its electronic counterparts, showing great potential in ultrafast and energy-free high-performance computing. Here, we propose an optical computing architecture enabled by on-chip diffraction to implement convolutional acceleration, termed optical convolution unit (OCU). We demonstrate that any real-valued convolution kernels can be exploited by OCU with a prominent computational throughput boosting via the concept of structral re-parameterization. With OCU as the fundamental unit, we build an optical convolutional neural network (oCNN) to implement two popular deep learning tasks: classification and regression. For classification, Fashion-MNIST and CIFAR-4 datasets are tested with accuracy of 91.63% and 86.25%, respectively. For regression, we build an optical denoising convolutional neural network (oDnCNN) to handle Gaussian noise in gray scale images with noise level {\sigma} = 10, 15, 20, resulting clean images with average PSNR of 31.70dB, 29.39dB and 27.72dB, respectively. The proposed OCU presents remarkable performance of low energy consumption and high information density due to its fully passive nature and compact footprint, providing a highly parallel while lightweight solution for future computing architecture to handle high dimensional tensors in deep learning.

Related papers

• Optical training of large-scale Transformers and deep neural networks with direct feedback alignment [48.90869997343841]
  We experimentally implement a versatile and scalable training algorithm, called direct feedback alignment, on a hybrid electronic-photonic platform. An optical processing unit performs large-scale random matrix multiplications, which is the central operation of this algorithm, at speeds up to 1500 TeraOps. We study the compute scaling of our hybrid optical approach, and demonstrate a potential advantage for ultra-deep and wide neural networks.
  arXiv  Detail & Related papers  (2024-09-01T12:48:47Z)
• LeRF: Learning Resampling Function for Adaptive and Efficient Image Interpolation [64.34935748707673]
  Recent deep neural networks (DNNs) have made impressive progress in performance by introducing learned data priors. We propose a novel method of Learning Resampling (termed LeRF) which takes advantage of both the structural priors learned by DNNs and the locally continuous assumption. LeRF assigns spatially varying resampling functions to input image pixels and learns to predict the shapes of these resampling functions with a neural network.
  arXiv  Detail & Related papers  (2024-07-13T16:09:45Z)
• All-optical graph representation learning using integrated diffractive photonic computing units [51.15389025760809]
  Photonic neural networks perform brain-inspired computations using photons instead of electrons. We propose an all-optical graph representation learning architecture, termed diffractive graph neural network (DGNN) We demonstrate the use of DGNN extracted features for node and graph-level classification tasks with benchmark databases and achieve superior performance.
  arXiv  Detail & Related papers  (2022-04-23T02:29:48Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• Silicon photonic subspace neural chip for hardware-efficient deep learning [11.374005508708995]
  optical neural network (ONN) is a promising candidate for next-generation neurocomputing. We devise a hardware-efficient photonic subspace neural network architecture. We experimentally demonstrate our PSNN on a butterfly-style programmable silicon photonic integrated circuit.
  arXiv  Detail & Related papers  (2021-11-11T06:34:05Z)
• Content-Aware Convolutional Neural Networks [98.97634685964819]
  Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. We propose a Content-aware Convolution (CAC) that automatically detects the smooth windows and applies a 1x1 convolutional kernel to replace the original large kernel.
  arXiv  Detail & Related papers  (2021-06-30T03:54:35Z)
• BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning [0.0]
  Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation algorithm (BP) BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction.
  arXiv  Detail & Related papers  (2021-02-19T20:00:21Z)
• Ensemble learning of diffractive optical networks [0.0]
  We numerically demonstrated that ensembles of N=14 and N=30 D2NNs achieve blind testing accuracies of 61.14% and 62.13%, respectively, on the classification of CIFAR-10 test images. These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset.
  arXiv  Detail & Related papers  (2020-09-15T05:02:50Z)
• Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit [38.898230519968116]
  We propose an optoelectronic reconfigurable computing paradigm by constructing a diffractive processing unit. It can efficiently support different neural networks and achieve a high model complexity with millions of neurons. Our prototype system built with off-the-shelf optoelectronic components surpasses the performance of state-of-the-art graphics processing units.
  arXiv  Detail & Related papers  (2020-08-26T16:34:58Z)
• Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning [56.83172249278467]
  We introduce Evolutionary Graph Reinforcement Learning (EGRL), a method designed for large search spaces. We train and validate our approach directly on the Intel NNP-I chip for inference. We additionally achieve 28-78% speed-up compared to the native NNP-I compiler on all three workloads.
  arXiv  Detail & Related papers  (2020-07-14T18:50:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.