A Review of Recent Advances of Binary Neural Networks for Edge Computing

• URL: http://arxiv.org/abs/2011.14824v1
• Date: Tue, 24 Nov 2020 01:10:21 GMT
• Title: A Review of Recent Advances of Binary Neural Networks for Edge Computing
• Authors: Wenyu Zhao, Teli Ma, Xuan Gong, Baochang Zhang, and David Doermann
• Abstract summary: This paper reviews advances on binary neural network (BNN) and 1-bit CNN technologies that are well suitable for front-end, edge-based computing. We also introduce applications in the areas of computer vision and speech recognition and discuss future applications for edge computing.
• Score: 15.646692826399438
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Edge computing is promising to become one of the next hottest topics in artificial intelligence because it benefits various evolving domains such as real-time unmanned aerial systems, industrial applications, and the demand for privacy protection. This paper reviews recent advances on binary neural network (BNN) and 1-bit CNN technologies that are well suitable for front-end, edge-based computing. We introduce and summarize existing work and classify them based on gradient approximation, quantization, architecture, loss functions, optimization method, and binary neural architecture search. We also introduce applications in the areas of computer vision and speech recognition and discuss future applications for edge computing.

Related papers

• Edge Intelligence with Spiking Neural Networks [50.33340747216377]
  Spiking Neural Networks (SNNs) offer low-power, event-driven computation on resource-constrained devices.<n>We present a systematic taxonomy of EdgeSNN foundations, encompassing neuron models, learning algorithms, and supporting hardware platforms.<n>Three representative practical considerations of EdgeSNN are discussed in depth: on-device inference using lightweight SNN models, resource-aware training and updating under non-stationary data conditions, and secure and privacy-preserving issues.
  arXiv  Detail & Related papers  (2025-07-18T16:47:52Z)
• Networking Systems for Video Anomaly Detection: A Tutorial and Survey [55.28514053969056]
  Video Anomaly Detection (VAD) is a fundamental research task within the Artificial Intelligence (AI) community. In this article, we delineate the foundational assumptions, learning frameworks, and applicable scenarios of various deep learning-driven VAD routes. We showcase our latest NSVAD research in industrial IoT and smart cities, along with an end-cloud collaborative architecture for deployable NSVAD.
  arXiv  Detail & Related papers  (2024-05-16T02:00:44Z)
• QuadraNet: Improving High-Order Neural Interaction Efficiency with Hardware-Aware Quadratic Neural Networks [28.130386036553144]
  Recent progress in computer vision-oriented neural network designs is mostly driven by capturing high-order neural interactions. We propose QuadraNet -- a comprehensive model design methodology from neuron reconstruction to structural block. We show that QuadraNet achieves up to 1.5$times$ throughput, 30% less memory footprint, and similar cognition performance, compared with the state-of-the-art high-order approaches.
  arXiv  Detail & Related papers  (2023-11-29T08:45:27Z)
• Edge-Cloud Polarization and Collaboration: A Comprehensive Survey [61.05059817550049]
  We conduct a systematic review for both cloud and edge AI. We are the first to set up the collaborative learning mechanism for cloud and edge modeling. We discuss potentials and practical experiences of some on-going advanced edge AI topics.
  arXiv  Detail & Related papers  (2021-11-11T05:58:23Z)
• Bringing AI To Edge: From Deep Learning's Perspective [7.308396023489246]
  Edge computing and artificial intelligence (AI) are gradually intersecting to build a novel system, called edge intelligence. One of these challenges is the textitcomputational gap between computation-intensive deep learning algorithms and less-capable edge systems. This paper surveys the representative and latest deep learning techniques that are useful for edge intelligence systems.
  arXiv  Detail & Related papers  (2020-11-25T12:07:21Z)
• One-step regression and classification with crosspoint resistive memory arrays [62.997667081978825]
  High speed, low energy computing machines are in demand to enable real-time artificial intelligence at the edge. One-step learning is supported by simulations of the prediction of the cost of a house in Boston and the training of a 2-layer neural network for MNIST digit recognition. Results are all obtained in one computational step, thanks to the physical, parallel, and analog computing within the crosspoint array.
  arXiv  Detail & Related papers  (2020-05-05T08:00:07Z)
• Spiking Neural Networks Hardware Implementations and Challenges: a Survey [53.429871539789445]
  Spiking Neural Networks are cognitive algorithms mimicking neuron and synapse operational principles. We present the state of the art of hardware implementations of spiking neural networks. We discuss the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level.
  arXiv  Detail & Related papers  (2020-05-04T13:24:00Z)
• Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing [25.16076541420544]
  Machine learning, particularly in the form of deep learning, has driven most of the recent fundamental developments in artificial intelligence. Deep learning has been successfully applied in areas such as object/pattern recognition, speech and natural language processing, self-driving vehicles, intelligent self-diagnostics tools, autonomous robots, knowledgeable personal assistants, and monitoring. This paper reviews the case for a novel beyond CMOS hardware technology, memristors, as a potential solution for the implementation of power-efficient in-memory computing, deep learning accelerators, and spiking neural networks.
  arXiv  Detail & Related papers  (2020-04-30T16:49:03Z)
• Binary Neural Networks: A Survey [126.67799882857656]
  The binary neural network serves as a promising technique for deploying deep models on resource-limited devices. The binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. We present a survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error.
  arXiv  Detail & Related papers  (2020-03-31T16:47:20Z)
• Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks [84.2155885234293]
  We first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC.
  arXiv  Detail & Related papers  (2020-02-22T14:38:11Z)

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.