Quantized Non-Volatile Nanomagnetic Synapse based Autoencoder for Efficient Unsupervised Network Anomaly Detection

• URL: http://arxiv.org/abs/2309.06449v1
• Date: Tue, 12 Sep 2023 02:29:09 GMT
• Title: Quantized Non-Volatile Nanomagnetic Synapse based Autoencoder for Efficient Unsupervised Network Anomaly Detection
• Authors: Muhammad Sabbir Alam, Walid Al Misba, Jayasimha Atulasimha
• Abstract summary: We show that implementing the autoencoder in edge devices capable of learning in real-time is challenging due to limited hardware, energy, and computational resources. We propose a ferromagnetic racetrack with engineered notches hosting a magnetic domain wall (DW) as the autoencoder synapses. Our DW-based approach demonstrates a remarkable reduction of at least three orders of magnitude in weight updates during training compared to the floating-point approach.
• Score: 0.07892577704654172
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the autoencoder based anomaly detection paradigm, implementing the autoencoder in edge devices capable of learning in real-time is exceedingly challenging due to limited hardware, energy, and computational resources. We show that these limitations can be addressed by designing an autoencoder with low-resolution non-volatile memory-based synapses and employing an effective quantized neural network learning algorithm. We propose a ferromagnetic racetrack with engineered notches hosting a magnetic domain wall (DW) as the autoencoder synapses, where limited state (5-state) synaptic weights are manipulated by spin orbit torque (SOT) current pulses. The performance of anomaly detection of the proposed autoencoder model is evaluated on the NSL-KDD dataset. Limited resolution and DW device stochasticity aware training of the autoencoder is performed, which yields comparable anomaly detection performance to the autoencoder having floating-point precision weights. While the limited number of quantized states and the inherent stochastic nature of DW synaptic weights in nanoscale devices are known to negatively impact the performance, our hardware-aware training algorithm is shown to leverage these imperfect device characteristics to generate an improvement in anomaly detection accuracy (90.98%) compared to accuracy obtained with floating-point trained weights. Furthermore, our DW-based approach demonstrates a remarkable reduction of at least three orders of magnitude in weight updates during training compared to the floating-point approach, implying substantial energy savings for our method. This work could stimulate the development of extremely energy efficient non-volatile multi-state synapse-based processors that can perform real-time training and inference on the edge with unsupervised data.

Related papers

• Cal-DETR: Calibrated Detection Transformer [67.75361289429013]
  We propose a mechanism for calibrated detection transformers (Cal-DETR), particularly for Deformable-DETR, UP-DETR and DINO. We develop an uncertainty-guided logit modulation mechanism that leverages the uncertainty to modulate the class logits. Results corroborate the effectiveness of Cal-DETR against the competing train-time methods in calibrating both in-domain and out-domain detections.
  arXiv  Detail & Related papers  (2023-11-06T22:13:10Z)
• A Cryogenic Memristive Neural Decoder for Fault-tolerant Quantum Error Correction [0.0]
  We design and analyze a neural decoder based on an in-memory crossbar (IMC) architecture. We develop hardware-aware re-training methods to mitigate the fidelity loss. This work provides a pathway to scalable, fast, and low-power cryogenic IMC hardware for integrated fault-tolerant QEC.
  arXiv  Detail & Related papers  (2023-07-18T17:46:33Z)
• Label-free timing analysis of SiPM-based modularized detectors with physics-constrained deep learning [9.234802409391111]
  We propose a novel method based on deep learning for timing analysis of modularized detectors. We mathematically demonstrate the existence of the optimal function desired by the method, and give a systematic algorithm for training and calibration of the model.
  arXiv  Detail & Related papers  (2023-04-24T09:16:31Z)
• The END: An Equivariant Neural Decoder for Quantum Error Correction [73.4384623973809]
  We introduce a data efficient neural decoder that exploits the symmetries of the problem. We propose a novel equivariant architecture that achieves state of the art accuracy compared to previous neural decoders.
  arXiv  Detail & Related papers  (2023-04-14T19:46:39Z)
• Deep Quantum Error Correction [73.54643419792453]
  Quantum error correction codes (QECC) are a key component for realizing the potential of quantum computing. In this work, we efficiently train novel emphend-to-end deep quantum error decoders. The proposed method demonstrates the power of neural decoders for QECC by achieving state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2023-01-27T08:16:26Z)
• Signal Detection in MIMO Systems with Hardware Imperfections: Message Passing on Neural Networks [101.59367762974371]
  In this paper, we investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments. It is difficult to train a deep neural network (DNN) with limited pilot signals, hindering its practical applications. We design an efficient message passing based Bayesian signal detector, leveraging the unitary approximate message passing (UAMP) algorithm.
  arXiv  Detail & Related papers  (2022-10-08T04:32:58Z)
• NAF: Neural Attenuation Fields for Sparse-View CBCT Reconstruction [79.13750275141139]
  This paper proposes a novel and fast self-supervised solution for sparse-view CBCT reconstruction. The desired attenuation coefficients are represented as a continuous function of 3D spatial coordinates, parameterized by a fully-connected deep neural network. A learning-based encoder entailing hash coding is adopted to help the network capture high-frequency details.
  arXiv  Detail & Related papers  (2022-09-29T04:06:00Z)
• Energy Efficient Learning with Low Resolution Stochastic Domain Wall Synapse Based Deep Neural Networks [0.9176056742068814]
  We demonstrate that extremely low resolution quantized (nominally 5-state) synapses with large variations in Domain Wall (DW) position can be both energy efficient and achieve reasonably high testing accuracies. We show that by implementing suitable modifications to the learning algorithms, we can address the behavior as well as the effect of their low-resolution to achieve high testing accuracies.
  arXiv  Detail & Related papers  (2021-11-14T09:12:29Z)
• AQD: Towards Accurate Fully-Quantized Object Detection [94.06347866374927]
  We propose an Accurate Quantized object Detection solution, termed AQD, to get rid of floating-point computation. Our AQD achieves comparable or even better performance compared with the full-precision counterpart under extremely low-bit schemes.
  arXiv  Detail & Related papers  (2020-07-14T09:07:29Z)
• High-level Modeling of Manufacturing Faults in Deep Neural Network Accelerators [2.6258269516366557]
  Google's Unit Processing (TPU) is a neural network accelerator that uses systolic array-based matrix multiplication hardware for computation in its crux. Manufacturing faults at any state element of the matrix multiplication unit can cause unexpected errors in these inference networks. We propose a formal model of permanent faults and their propagation in a TPU using the Discrete-Time Markov Chain (DTMC) formalism.
  arXiv  Detail & Related papers  (2020-06-05T18:11:14Z)
• QUANOS- Adversarial Noise Sensitivity Driven Hybrid Quantization of Neural Networks [3.2242513084255036]
  QUANOS is a framework that performs layer-specific hybrid quantization based on Adversarial Noise Sensitivity (ANS) Our experiments on CIFAR10, CIFAR100 datasets show that QUANOS outperforms homogenously quantized 8-bit precision baseline in terms of adversarial robustness.
  arXiv  Detail & Related papers  (2020-04-22T15:56:31Z)

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.