DistriHD: A Memory Efficient Distributed Binary Hyperdimensional Computing Architecture for Image Classification

Abstract

Hyper-Dimensional (HD) computing is a brain-inspired learning approach for efficient and fast learning on today's embedded devices. HD computing first encodes all data points to high-dimensional vectors called hypervectors and then efficiently performs the classification task using a well-defined set of operations. Although HD computing achieved reasonable performances in several practical tasks, it comes with huge memory requirements since the data point should be stored in a very long vector having thousands of bits. To alleviate this problem, we propose a novel HD computing architecture, called DistriHD which enables HD computing to be trained and tested using binary hypervectors and achieves high accuracy in single-pass training mode with significantly low hardware resources. DistriHD encodes data points to distributed binary hypervectors and eliminates the expensive item memory in the encoder, which significantly reduces the required hardware cost for inference. Our evaluation also shows that our model can achieve a $27.6\times$ reduction in memory cost without hurting the classification accuracy. The hardware implementation also demonstrates that DistriHD achieves over $9.9\times$ and $28.8\times$ reduction in area and power, respectively.