In this work, we present ODHD, an algorithm for outlier detection based on hyperdimensional computing (HDC), a non-classical learning paradigm. Along with the HDC-based algorithm, we propose IM-ODHD, a computing-in-memory (CiM) implementation based on hardware/software (HW/SW) codesign for improved latency and energy efficiency. The training and testing phases of ODHD may be performed with conventional CPU/GPU hardware or our IM-ODHD, SRAM-based CiM architecture using the proposed HW/SW codesign techniques. We evaluate the performance of ODHD on six datasets from different application domains using three metrics, namely accuracy, F1 score, and ROC-AUC, and compare it with multiple baseline methods such as OCSVM, isolation forest, and autoencoder. The experimental results indicate that ODHD outperforms all the baseline methods in terms of these three metrics on every dataset for both CPU/GPU and CiM implementations. Furthermore, we perform an extensive design space exploration to demonstrate the tradeoff between delay, energy efficiency, and performance of ODHD. We demonstrate that the HW/SW codesign implementation of the outlier detection on IM-ODHD is able to outperform the GPU-based implementation of ODHD by at least 331.5x/889x in terms of training/testing latency (and on average 14.0x/36.9x in terms of training/testing energy consumption.

翻译：本文提出ODHD算法，一种基于超维计算（HDC）的非经典学习范式用于异常检测。伴随该HDC算法，我们提出IM-ODHD，一种基于硬件/软件（HW/SW）协同设计的存内计算（CiM）实现方案，以优化延迟与能效。ODHD的训练与测试阶段可在传统CPU/GPU硬件上运行，亦可采用我们的IM-ODHD——基于SRAM的CiM架构，并融合所提出的HW/SW协同设计技术。我们使用准确率、F1分数和ROC-AUC三项指标，评估ODHD在六个来自不同应用领域数据集上的表现，并与OCSVM、孤立森林和自编码器等多种基线方法进行对比。实验结果表明，在所有数据集上，无论是CPU/GPU实现还是CiM实现，ODHD在这三项指标上均优于所有基线方法。此外，我们通过广泛的设计空间探索，揭示了ODHD在延迟、能效与性能之间的权衡关系。实验证明，基于IM-ODHD的HW/SW协同设计异常检测实现，在训练/测试延迟上比基于GPU的ODHD实现至少提升331.5倍/889倍（平均训练/测试能耗节省14.0倍/36.9倍）。