Multi-label HD Classification in 3D Flash

Many classification problems in practice map each sample to more than one label - this is known as multi-label classification. In this work, we present Multi-label HD, an in 3D storage multi-label classification system that uses Hyperdimensional Computing (HD). Multi-label HD is the first HD system to support multi-label classification. We propose two different mappings of HD to Multi-label HD. The first, Power Set HD, transforms the multi-label problem into single-label classification by creating a new class for each label combination. The second, Multi-Model HD, creates a binary classification model for each possible label. Our evaluation shows that Multi-Model HD achieves, on average,
$47.8\times$
higher energy efficiency and
$47.1\times$
faster execution time while achieving 5% higher classification accuracy as state-of-the-art light-weight multi-label classifiers. Power Set HD achieves 13% higher accuracy than Multi-Model HD, but is
$2\times$
slower. Our 3D-flash acceleration further improves the energy efficiency of Multi-label HD training by
$228\times$
and reduces the latency by
$610\times$
vs training on a CPU.