A Computing-in-Memory-Based One-Class Hyperdimensional Computing Model for Outlier Detection

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 331.5
$\times$
/889
$\times$
in terms of training/testing latency (and on average 14.0
$\times$
/36.9
$\times$
in terms of training/testing energy consumption).