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 293x/419x in terms of training/testing latency (and on average 16.0x/15.9x in terms of training/testing energy consumption).

翻译：本文提出ODHD，一种基于超维度计算（HDC）这一非经典学习范式的异常检测算法。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，旨在提升延迟与能效。ODHD的训练与测试阶段既可在传统CPU/GPU硬件上执行，也可采用我们所提出的基于HW/SW协同设计技术的SRAM型CiM架构（IM-ODHD）来完成。我们使用准确率、F1分数和ROC-AUC三种指标，在来自不同应用领域的六个数据集上评估ODHD的性能，并将其与OCSVM、孤立森林和自编码器等多种基线方法进行比较。实验结果表明，在所有数据集的CPU/GPU与CiM实现中，ODHD均在这些指标上优于所有基线方法。此外，我们进行了广泛的设计空间探索，以展示ODHD在延迟、能效与性能之间的权衡。我们证明，IM-ODHD上基于HW/SW协同设计的异常检测实现，在训练/测试延迟方面至少比基于GPU的ODHD实现提升293倍/419倍（在训练/测试能耗方面平均提升16.0倍/15.9倍）。