(Presenting Fri. 3/15/19 at 2:00PM ET)
Authors: Mohsen Imani, John Messerly, Fan Wu, Wang Pi,
and Tajana S. Rosing
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.
