A Binary Learning Framework for Hyperdimensional Computing
Mohsen Imani1,a, John Messerly1,b, Fan Wu2, Wang Pi3 and Tajana Rosing1,c
1Computer Science and Engineering Department, UC San Diego, La Jolla, USA
amoimani@ucsd.edu
bjmesserl@ucsd.edu
ctajana@ucsd.edu
2Department of Computer Science and Engineering, UC Riverside, Riverside
3School of Electronics Engineering and Computer Science, Peking University, Beijing, P.R.China
piwang@pku.edu.cn
ABSTRACT
Brain-inspired Hyperdimensional (HD) computing is a computing paradigm emulating a neuron’s activity in highdimensional space. In practice, HD first encodes all data points to high-dimensional vectors, called hypervectors, and then performs the classification task in an efficient way using a well-defined set of operations. In order to provide acceptable classification accuracy, the current HD computing algorithms need to map data points to hypervectors with non-binary elements. However, working with non-binary vectors significantly increases the HD computation cost and the amount of memory requirement for both training and inference. In this paper, we propose BinHD, a novel learning framework which enables HD computing to be trained and tested using binary hypervectors. BinHD encodes data points to binary hypervectors and provides a framework which enables HD to perform the training task with significantly low resources and memory footprint. In inference, BinHD binarizes the model and simplifies the costly Cosine similarity used in existing HD computing algorithms to a hardware-friendly Hamming distance metric. In addition, for the first time, BinHD introduces the concept of learning rate in HD computing which gives an extra knob to the HD in order to control the training efficiency and accuracy.We accordingly design a digital hardware to accelerate BinHD computation. Our evaluations on four practical classification applications show that BinHD in training (inference) can achieve 12.4× and 6.3× (13.8× and 9.9×) energy efficiency and speedup as compared to the state-of-the-art HD computing algorithm while providing the similar classification accuracy.
Keywords: Brain-inspired computing, Energy-efficiency classification, Hyperdimensional computing.
