Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge

• URL: http://arxiv.org/abs/2110.14357v2
• Date: Tue, 18 Apr 2023 03:22:29 GMT
• Title: Binarized ResNet: Enabling Robust Automatic Modulation Classification at the resource-constrained Edge
• Abstract summary: Deep neural networks (DNNs) have been used extensively for automatic modulation classification (AMC) DNNs have high memory and computation requirements making them impractical for edge networks where the devices are resource-constrained. This work proposes a rotated binary large ResNet (RBLResNet) for AMC that can be deployed at the edge network because of low memory and computational complexity.
• Score: 9.103162893345939
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, deep neural networks (DNNs) have been used extensively for automatic modulation classification (AMC), and the results have been quite promising. However, DNNs have high memory and computation requirements making them impractical for edge networks where the devices are resource-constrained. They are also vulnerable to adversarial attacks, which is a significant security concern. This work proposes a rotated binary large ResNet (RBLResNet) for AMC that can be deployed at the edge network because of low memory and computational complexity. The performance gap between the RBLResNet and existing architectures with floating-point weights and activations can be closed by two proposed ensemble methods: (i) multilevel classification (MC), and (ii) bagging multiple RBLResNets while retaining low memory and computational power. The MC method achieves an accuracy of $93.39\%$ at $10$dB over all the $24$ modulation classes of the Deepsig dataset. This performance is comparable to state-of-the-art (SOTA) performances, with $4.75$ times lower memory and $1214$ times lower computation. Furthermore, RBLResNet also has high adversarial robustness compared to existing DNN models. The proposed MC method with RBLResNets has an adversarial accuracy of $87.25\%$ over a wide range of SNRs, surpassing the robustness of all existing SOTA methods to the best of our knowledge. Properties such as low memory, low computation, and the highest adversarial robustness make it a better choice for robust AMC in low-power edge devices.

Related papers

• Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems [3.1567687557500297]
  Narrowband interference (NBI) severely degrades frequency-division multiplexing (OFDM) systems.<n>We use a unified deep learning framework for joint NBI cancellation and robust soft demodulation.
  arXiv  Detail & Related papers  (2026-07-09T17:26:43Z)
• M$^2$RNN: Non-Linear RNNs with Matrix-Valued States for Scalable Language Modeling [53.351103753419046]
  We introduce Matrix-to-Matrix RNN: an architecture with matrix-valued hidden states and expressive non-linear state transitions.<n>We demonstrate that the language modeling performance of non-linear RNNs is limited by their state size.<n>We also demonstrate how the state size expansion mechanism enables efficient use of tensor cores.
  arXiv  Detail & Related papers  (2026-03-15T12:53:09Z)
• S$^2$NN: Sub-bit Spiking Neural Networks [53.08060832135342]
  Spiking Neural Networks (SNNs) offer an energy-efficient paradigm for machine intelligence.<n>Despite recent advances in binary SNNs, the storage and computational demands remain substantial for large-scale networks.<n>We propose Sub-bit Spiking Neural Networks (S$2$NNs) that represent weights with less than one bit.
  arXiv  Detail & Related papers  (2025-09-29T04:17:44Z)
• ALWNN Empowered Automatic Modulation Classification: Conquering Complexity and Scarce Sample Conditions [24.59462798452397]
  This paper proposes an automatic modulation classification model based on the Adaptive Lightweight Wavelet Neural Network (ALWNN) and the few-shot framework (MALWNN) The ALWNN model, by integrating the adaptive wavelet neural network and depth separable convolution, reduces the number of model parameters and computational complexity. Experiments with MALWNN show its superior performance in few-shot learning scenarios compared to other algorithms.
  arXiv  Detail & Related papers  (2025-03-24T06:14:33Z)
• SLTNet: Efficient Event-based Semantic Segmentation with Spike-driven Lightweight Transformer-based Networks [7.234661153788162]
  Event-based semantic segmentation has great potential in autonomous driving and robotics. Current artificial neural network (ANN)-based segmentation methods suffer from high computational demands, the requirements for image frames, and massive energy consumption. We introduce SLTNet, a spike-driven lightweight transformer-based network designed for event-based semantic segmentation.
  arXiv  Detail & Related papers  (2024-12-17T12:11:04Z)
• GhostRNN: Reducing State Redundancy in RNN with Cheap Operations [66.14054138609355]
  We propose an efficient RNN architecture, GhostRNN, which reduces hidden state redundancy with cheap operations. Experiments on KWS and SE tasks demonstrate that the proposed GhostRNN significantly reduces the memory usage (40%) and computation cost while keeping performance similar.
  arXiv  Detail & Related papers  (2024-11-20T11:37:14Z)
• 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)
• Compact Multi-level Sparse Neural Networks with Input Independent Dynamic Rerouting [33.35713740886292]
  Sparse deep neural networks can substantially reduce the complexity and memory consumption of the models. Facing the real-life challenges, we propose to train a sparse model that supports multiple sparse levels. In this way, one can dynamically select the appropriate sparsity level during inference, while the storage cost is capped by the least sparse sub-model.
  arXiv  Detail & Related papers  (2021-12-21T01:35:51Z)
• Elastic-Link for Binarized Neural Network [9.83865304744923]
  "Elastic-Link" (EL) module enrich information flow within a BNN by adaptively adding real-valued input features to the subsequent convolutional output features. EL produces a significant improvement on the challenging large-scale ImageNet dataset. With the integration of ReActNet, it yields a new state-of-the-art result of 71.9% top-1 accuracy.
  arXiv  Detail & Related papers  (2021-12-19T13:49:29Z)
• Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach [60.67748036747221]
  Implicit neural networks offer competitive performance and reduced memory consumption. They can remain brittle with respect to input adversarial perturbations. This paper proposes a theoretical and computational framework for robustness verification of implicit neural networks.
  arXiv  Detail & Related papers  (2021-12-10T03:08:55Z)
• 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)
• ShiftAddNet: A Hardware-Inspired Deep Network [87.18216601210763]
  ShiftAddNet is an energy-efficient multiplication-less deep neural network. It leads to both energy-efficient inference and training, without compromising expressive capacity. ShiftAddNet aggressively reduces over 80% hardware-quantified energy cost of DNNs training and inference, while offering comparable or better accuracies.
  arXiv  Detail & Related papers  (2020-10-24T05:09:14Z)
• Fully Dynamic Inference with Deep Neural Networks [19.833242253397206]
  Two compact networks, called Layer-Net (L-Net) and Channel-Net (C-Net), predict on a per-instance basis which layers or filters/channels are redundant and therefore should be skipped. On the CIFAR-10 dataset, LC-Net results in up to 11.9$times$ fewer floating-point operations (FLOPs) and up to 3.3% higher accuracy compared to other dynamic inference methods. On the ImageNet dataset, LC-Net achieves up to 1.4$times$ fewer FLOPs and up to 4.6% higher Top-1 accuracy than the other methods.
  arXiv  Detail & Related papers  (2020-07-29T23:17:48Z)
• RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference [24.351577383531616]
  We introduce RNNPool, a novel pooling operator based on Recurrent Neural Networks (RNNs) An RNNPool layer can effectively replace multiple blocks in a variety of architectures like MobileNets, DenseNet when applied to standard vision tasks like image classification and face detection. We use RNNPool with the standard S3FD architecture to construct a face detection method that achieves state-of-the-art MAP for tiny ARM Cortex-M4 class microcontrollers with under 256 KB of RAM.
  arXiv  Detail & Related papers  (2020-02-27T05:22:44Z)
• PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning [57.20262984116752]
  We introduce a new dimension, fine-grained pruning patterns inside the coarse-grained structures, revealing a previously unknown point in design space. With the higher accuracy enabled by fine-grained pruning patterns, the unique insight is to use the compiler to re-gain and guarantee high hardware efficiency.
  arXiv  Detail & Related papers  (2020-01-01T04:52:07Z)

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.