基于量化非易失性纳米磁突触的自编码器用于高效无监督网络异常检测
Quantized Non-Volatile Nanomagnetic Synapse based Autoencoder for Efficient Unsupervised Network Anomaly Detection
AI总结：
针对边缘设备实现自编码器异常检测的硬件资源限制问题，提出基于铁磁赛道磁畴壁突触的5态量化自编码器，通过硬件感知训练提升性能并减少权重更新，推动节能边缘实时检测处理器发展。
摘要：
在基于自编码器的异常检测范式中，由于硬件、能源和计算资源有限，在能够实时学习的边缘设备中实现自编码器极具挑战性。我们表明，通过设计具有低分辨率非易失性存储器突触的自编码器并采用有效的量化神经网络学习算法，可以解决这些限制。我们提出一种带有工程化缺口的铁磁赛道，其承载磁畴壁（DW）作为自编码器突触，其中有限状态（5态）突触权重由自旋轨道力矩（SOT）电流脉冲控制。所提出的自编码器模型的异常检测性能在NSL-KDD数据集上进行了评估。我们执行了考虑有限分辨率和DW器件随机性的自编码器训练，其产生的异常检测性能与具有浮点精度权重的自编码器相当。虽然已知纳米级器件中有限数量的量化状态和DW突触权重的固有随机性会对性能产生负面影响，但我们的硬件感知训练算法被证明可利用这些不完善的器件特性，使异常检测精度（90.98%）相比浮点训练权重获得的精度有所提升。此外，与浮点方法相比，我们基于DW的方法在训练期间的权重更新显著减少了至少三个数量级，这意味着我们的方法可实现大幅节能。这项工作可能会推动极其节能的非易失性多态突触处理器的发展，这类处理器能够在边缘设备上利用无监督数据进行实时训练和推理。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
