Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design

• URL: http://arxiv.org/abs/2012.08906v2
• Date: Thu, 1 Apr 2021 20:15:41 GMT
• Title: Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design
• Authors: Yingjie Li, Ruiyang Chen, Berardi Sensale Rodriguez, Weilu Gao, and Cunxi Yu
• Abstract summary: 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.
• Score: 1.6066483376871004
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing efforts on optical neural networks and optical computing based DNNs hardware, which bring significant advantages for deep learning systems in terms of their power efficiency, parallelism and computational speed. Among them, free-space diffractive deep neural networks (D$^2$NNs) based on the light diffraction, feature millions of neurons in each layer interconnected with neurons in neighboring layers. However, due to the challenge of implementing reconfigurability, deploying different DNNs algorithms requires re-building and duplicating the physical diffractive systems, which significantly degrades the hardware efficiency in practical application scenarios. Thus, 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 under wide noise ranges of all system components. In addition, we propose a domain-specific regularization algorithm for training the proposed multi-task architecture, which can be used to flexibly adjust the desired performance for each task.

Related papers

• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  In neuromorphic computing, spiking neural networks (SNNs) perform inference tasks, offering significant efficiency gains for workloads involving sequential data. Recent advances in hardware and software have demonstrated that embedding a few bits of payload in each spike exchanged between the spiking neurons can further enhance inference accuracy. This paper investigates a wireless neuromorphic split computing architecture employing multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Sparsity-Aware Hardware-Software Co-Design of Spiking Neural Networks: An Overview [1.0499611180329804]
  Spiking Neural Networks (SNNs) are inspired by the sparse and event-driven nature of biological neural processing, and offer the potential for ultra-low-power artificial intelligence. We explore the hardware-software co-design of sparse SNNs, examining how sparsity representation, hardware architectures, and training techniques influence hardware efficiency. Our work aims to illuminate the path towards embedded neuromorphic systems that fully exploit the computational advantages of sparse SNNs.
  arXiv  Detail & Related papers  (2024-08-26T17:22:11Z)
• DNN Partitioning, Task Offloading, and Resource Allocation in Dynamic Vehicular Networks: A Lyapunov-Guided Diffusion-Based Reinforcement Learning Approach [49.56404236394601]
  We formulate the problem of joint DNN partitioning, task offloading, and resource allocation in Vehicular Edge Computing. Our objective is to minimize the DNN-based task completion time while guaranteeing the system stability over time. We propose a Multi-Agent Diffusion-based Deep Reinforcement Learning (MAD2RL) algorithm, incorporating the innovative use of diffusion models.
  arXiv  Detail & Related papers  (2024-06-11T06:31:03Z)
• 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)
• A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading [62.34538208323411]
  We propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs) MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.
  arXiv  Detail & Related papers  (2023-09-02T11:01:16Z)
• Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture [14.55785957623462]
  This work presents a novel ONNs architecture, namely, textitRubikONNs, which utilizes the physical properties of optical systems to encode multiple feed-forward functions. Our experimental results demonstrate more than 4$times$ improvements in energy and cost efficiency with marginal accuracy compared to the state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-04-25T16:51:12Z)
• Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications [8.434224141580758]
  Split-Et-Impera determines the set of the best-split points of a neural network based on deep network interpretability principles. It performs a communication-aware simulation for the rapid evaluation of different neural network rearrangements. It suggests the best match between the quality of service requirements of the application and the performance in terms of accuracy and latency time.
  arXiv  Detail & Related papers  (2023-03-22T13:00:00Z)
• 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)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)
• Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead [14.313423044185583]
  This paper introduces the key properties of two brain-inspired models like Deep Neural Network (DNN), and then analyzes techniques to produce efficient and high-performance designs. A single inference of a DL model may require billions of multiply-and-accumulated operations, making the DL extremely compute- and energy-hungry.
  arXiv  Detail & Related papers  (2020-12-21T10:27:48Z)
• Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks [80.15411508088522]
  Spiking neural networks (SNNs) have shown advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency. We propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition.
  arXiv  Detail & Related papers  (2020-07-02T15:38:44Z)

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.