Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit

• URL: http://arxiv.org/abs/2008.11659v1
• Date: Wed, 26 Aug 2020 16:34:58 GMT
• Title: Large-scale neuromorphic optoelectronic computing with a reconfigurable diffractive processing unit
• Authors: Tiankuang Zhou, Xing Lin, Jiamin Wu, Yitong Chen, Hao Xie, Yipeng Li, Jintao Fan, Huaqiang Wu, Lu Fang and Qionghai Dai
• Abstract summary: 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.
• Score: 38.898230519968116
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Application-specific optical processors have been considered disruptive technologies for modern computing that can fundamentally accelerate the development of artificial intelligence (AI) by offering substantially improved computing performance. Recent advancements in optical neural network architectures for neural information processing have been applied to perform various machine learning tasks. However, the existing architectures have limited complexity and performance; and each of them requires its own dedicated design that cannot be reconfigured to switch between different neural network models for different applications after deployment. Here, we propose an optoelectronic reconfigurable computing paradigm by constructing a diffractive processing unit (DPU) that can efficiently support different neural networks and achieve a high model complexity with millions of neurons. It allocates almost all of its computational operations optically and achieves extremely high speed of data modulation and large-scale network parameter updating by dynamically programming optical modulators and photodetectors. We demonstrated the reconfiguration of the DPU to implement various diffractive feedforward and recurrent neural networks and developed a novel adaptive training approach to circumvent the system imperfections. We applied the trained networks for high-speed classifying of handwritten digit images and human action videos over benchmark datasets, and the experimental results revealed a comparable classification accuracy to the electronic computing approaches. Furthermore, our prototype system built with off-the-shelf optoelectronic components surpasses the performance of state-of-the-art graphics processing units (GPUs) by several times on computing speed and more than an order of magnitude on system energy efficiency.

Related papers

• Mechanistic Neural Networks for Scientific Machine Learning [58.99592521721158]
  We present Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations. Central to our approach is a novel Relaxed Linear Programming solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs.
  arXiv  Detail & Related papers  (2024-02-20T15:23:24Z)
• Random resistive memory-based deep extreme point learning machine for unified visual processing [67.51600474104171]
  We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM) Our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems.
  arXiv  Detail & Related papers  (2023-12-14T09:46:16Z)
• Free-Space Optical Spiking Neural Network [0.0]
  We introduce the Free-space Optical deep Spiking Convolutional Neural Network (OSCNN) This novel approach draws inspiration from computational models of the human eye. Our results demonstrate promising performance with minimal latency and power consumption compared to their electronic ONN counterparts.
  arXiv  Detail & Related papers  (2023-11-08T09:41:14Z)
• Sophisticated deep learning with on-chip optical diffractive tensor processing [5.081061839052458]
  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.
  arXiv  Detail & Related papers  (2022-12-20T03:33:26Z)
• Intelligence Processing Units Accelerate Neuromorphic Learning [52.952192990802345]
  Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy consumption and latency. We present an IPU-optimized release of our custom SNN Python package, snnTorch.
  arXiv  Detail & Related papers  (2022-11-19T15:44:08Z)
• 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)
• FPGA-optimized Hardware acceleration for Spiking Neural Networks [69.49429223251178]
  This work presents the development of a hardware accelerator for an SNN, with off-line training, applied to an image recognition task. The design targets a Xilinx Artix-7 FPGA, using in total around the 40% of the available hardware resources. It reduces the classification time by three orders of magnitude, with a small 4.5% impact on the accuracy, if compared to its software, full precision counterpart.
  arXiv  Detail & Related papers  (2022-01-18T13:59:22Z)
• Monolithic Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment [0.6501025489527172]
  We propose on-chip training of neural networks enabled by a CMOS-compatible silicon photonic architecture. Our scheme employs the direct feedback alignment training algorithm, which trains neural networks using error feedback rather than error backpropagation. We experimentally demonstrate training a deep neural network with the MNIST dataset using on-chip MAC operation results.
  arXiv  Detail & Related papers  (2021-11-12T18:31:51Z)
• Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design [1.6066483376871004]
  This work proposes a novel hardware-software co-design method that enables robust and noise-resilient Multi-task Learning in D$2$NNs. Our experimental results demonstrate significant improvements in versatility and hardware efficiency, and also demonstrate the robustness of proposed multi-task D$2$NN architecture.
  arXiv  Detail & Related papers  (2020-12-16T12:29:54Z)
• Ultra-Low-Power FDSOI Neural Circuits for Extreme-Edge Neuromorphic Intelligence [2.6199663901387997]
  In-memory computing mixed-signal neuromorphic architectures provide promising ultra-low-power solutions for edge-computing sensory-processing applications. We present a set of mixed-signal analog/digital circuits that exploit the features of advanced Fully-Depleted Silicon on Insulator (FDSOI) integration processes.
  arXiv  Detail & Related papers  (2020-06-25T09:31:29Z)

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.