Binarized Neural Networks for Resource-Constrained On-Device Gait Identification

• URL: http://arxiv.org/abs/2103.16609v1
• Date: Tue, 30 Mar 2021 18:29:23 GMT
• Title: Binarized Neural Networks for Resource-Constrained On-Device Gait Identification
• Authors: Daniel J. Wu, Avoy Datta and Vinay Prabhu
• Abstract summary: We show that binarized neural networks can act as robust discriminators, maintaining both an acceptable level of accuracy while also dramatically decreasing memory requirements. We propose BiPedalNet, a compact CNN that nearly matches the state-of-the-art on the Padova gait dataset, with only 1/32 of the memory overhead.
• Score: 1.933681537640272
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: User authentication through gait analysis is a promising application of discriminative neural networks -- particularly due to the ubiquity of the primary sources of gait accelerometry, in-pocket cellphones. However, conventional machine learning models are often too large and computationally expensive to enable inference on low-resource mobile devices. We propose that binarized neural networks can act as robust discriminators, maintaining both an acceptable level of accuracy while also dramatically decreasing memory requirements, thereby enabling on-device inference. To this end, we propose BiPedalNet, a compact CNN that nearly matches the state-of-the-art on the Padova gait dataset, with only 1/32 of the memory overhead.

Related papers

• Enabling On-device Continual Learning with Binary Neural Networks [3.180732240499359]
  We propose a solution that combines recent advancements in the field of Continual Learning (CL) and Binary Neural Networks (BNNs) Specifically, our approach leverages binary latent replay activations and a novel quantization scheme that significantly reduces the number of bits required for gradient computation.
  arXiv  Detail & Related papers  (2024-01-18T11:57:05Z)
• Quantization-aware Neural Architectural Search for Intrusion Detection [5.010685611319813]
  We present a design methodology that automatically trains and evolves quantized neural network (NN) models that are a thousand times smaller than state-of-the-art NNs. The number of LUTs utilized by this network when deployed to an FPGA is between 2.3x and 8.5x smaller with performance comparable to prior work.
  arXiv  Detail & Related papers  (2023-11-07T18:35:29Z)
• Heterogenous Memory Augmented Neural Networks [84.29338268789684]
  We introduce a novel heterogeneous memory augmentation approach for neural networks. By introducing learnable memory tokens with attention mechanism, we can effectively boost performance without huge computational overhead. We show our approach on various image and graph-based tasks under both in-distribution (ID) and out-of-distribution (OOD) conditions.
  arXiv  Detail & Related papers  (2023-10-17T01:05:28Z)
• Robust Training and Verification of Implicit Neural Networks: A Non-Euclidean Contractive Approach [64.23331120621118]
  This paper proposes a theoretical and computational framework for training and robustness verification of implicit neural networks. We introduce a related embedded network and show that the embedded network can be used to provide an $ell_infty$-norm box over-approximation of the reachable sets of the original network. We apply our algorithms to train implicit neural networks on the MNIST dataset and compare the robustness of our models with the models trained via existing approaches in the literature.
  arXiv  Detail & Related papers  (2022-08-08T03:13:24Z)
• Recurrent neural networks that generalize from examples and optimize by dreaming [0.0]
  We introduce a generalized Hopfield network where pairwise couplings between neurons are built according to Hebb's prescription for on-line learning. We let the network experience solely a dataset made of a sample of noisy examples for each pattern. Remarkably, the sleeping mechanisms always significantly reduce the dataset size required to correctly generalize.
  arXiv  Detail & Related papers  (2022-04-17T08:40:54Z)
• Quantized Neural Networks via {-1, +1} Encoding Decomposition and Acceleration [83.84684675841167]
  We propose a novel encoding scheme using -1, +1 to decompose quantized neural networks (QNNs) into multi-branch binary networks. We validate the effectiveness of our method on large-scale image classification, object detection, and semantic segmentation tasks.
  arXiv  Detail & Related papers  (2021-06-18T03:11:15Z)
• Binary Neural Network for Speaker Verification [13.472791713805762]
  This paper focuses on how to apply binary neural networks to the task of speaker verification. Experiment results show that, after binarizing the Convolutional Neural Network, the ResNet34-based network achieves an EER of around 5%.
  arXiv  Detail & Related papers  (2021-04-06T06:04:57Z)
• Enabling Binary Neural Network Training on the Edge [7.32770338248516]
  Existing binary neural network training methods require concurrent storage of high-precision activations for all layers. We introduce a low-cost binary neural network training strategy exhibiting sizable memory footprint reductions. We also demonstrate from-scratch ImageNet training of binarized ResNet-18, achieving a 3.78$times$ memory reduction.
  arXiv  Detail & Related papers  (2021-02-08T15:06:41Z)
• Binary Graph Neural Networks [69.51765073772226]
  Graph Neural Networks (GNNs) have emerged as a powerful and flexible framework for representation learning on irregular data. In this paper, we present and evaluate different strategies for the binarization of graph neural networks. We show that through careful design of the models, and control of the training process, binary graph neural networks can be trained at only a moderate cost in accuracy on challenging benchmarks.
  arXiv  Detail & Related papers  (2020-12-31T18:48:58Z)
• Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming [97.40955121478716]
  We propose a first-order dual SDP algorithm that requires memory only linear in the total number of network activations. We significantly improve L-inf verified robust accuracy from 1% to 88% and 6% to 40% respectively. We also demonstrate tight verification of a quadratic stability specification for the decoder of a variational autoencoder.
  arXiv  Detail & Related papers  (2020-10-22T12:32:29Z)
• Reservoir Memory Machines as Neural Computers [70.5993855765376]
  Differentiable neural computers extend artificial neural networks with an explicit memory without interference. We achieve some of the computational capabilities of differentiable neural computers with a model that can be trained very efficiently.
  arXiv  Detail & Related papers  (2020-09-14T12:01:30Z)

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.