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.

基于自动编码器的异常检测范式中，依靠具有实时学习能力的边缘设备实现自动编码器是一项极富挑战性的任务。通过设计具有低分辨率的非挥发性存储器型突触的自动编码器，并采用一种有效的量化神经网络学习算法，我们证明这些限制可以得到解决。我们提出了一种带有工程化凹槽的铁磁记录道，以磁域壁作为自动编码器的突触，其中通过自旋轨道涡流脉冲来操作有限状态（5 状态）的突触权重。我们在 NSL-KDD 数据集上评估了所提出的自动编码器模型对异常检测的性能。通过对突触进行有限分辨率和 DW 器件随机性感知的训练，我们得到了与拥有浮点精度权重的自动编码器相当的异常检测性能。虽然量化状态的有限数量和纳米尺度器件中固有的随机性对性能有负面影响，但我们的面向硬件的训练算法被证明可以利用这些不完善的器件特性，从而提高异常检测的准确性（90.98%），相较于使用浮点训练权重的准确性。此外，我们的 DW-based 方法在训练过程中相较于浮点训练方法重要减少了至少三个数量级的权重更新，意味着我们的方法可以大大节省能量。这项工作有望推动非挥发多状态突触型处理器的极高能效、能够在边缘上对无监督数据进行实时训练和推断的发展。