Brain-inspired HyperDimensional Computing
(HDC) is an alternative computation model working based
on the observation that the human brain operates on highdimensional
representations of data. Existing HDC solutions rely
on expensive pre-processing algorithms for feature extraction.
In this paper, we propose StocHD, a novel end-to-end
hyperdimensional system that supports accurate, efficient,
and robust learning over raw data. StocHD expands HDC
functionality to the computing area by mathematically defining
stochastic arithmetic over HDC hypervectors. StocHD enables
an entire learning application (including feature extractor)
to process using HDC data representation, enabling uniform,
efficient, robust, and highly parallel computation. We also
propose a novel fully digital and scalable Processing In-Memory
(PIM) architecture that exploits the HDC memory-centric
nature to support extensively parallel computation.
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StocHD: Stochastic Hyperdimensional System for Efficient and Robust Learning from Raw Data
Abstract—Hyperdimensional Computing (HDC) is a neurallyinspired computation model working based on the observation that the human brain operates on high-dimensional representations of data, called hypervector. Although HDC is significantly powerful in reasoning and association of the abstract information, it is weak on features extraction from complex data such as image/video. As a result, most existing HDC solutions rely on expensive pre-processing algorithms for feature extraction. In this paper, we propose StocHD, a novel end-to-end hyperdimensional system that supports accurate, efficient, and robust learning over
raw data. Unlike prior work that used HDC for learning tasks, StocHD expands HDC functionality to the computing area by mathematically defining stochastic arithmetic over HDC hypervectors. StocHD enables an entire learning application (including feature extractor) to process using HDC data representation, enabling
uniform, efficient, robust, and highly parallel computation.
We also propose a novel fully digital and scalable Processing
In-Memory (PIM) architecture that exploits the HDC memorycentric
nature to support extensively parallel computation. Our
evaluation over a wide range of classification tasks shows that
StocHD provides, on average, 3.3x and 6.4x (52.3x and 143.Sx)
faster and higher energy efficiency as compared to state-of-the-art
HDC algorithm running on PIM (NVIDIA GPU), while providing
16x higher computational robustness.
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- Award ID(s):
- 2019511
- NSF-PAR ID:
- 10338284
- Date Published:
- Journal Name:
- Proceedings of 58th ACM/IEEE Design Automation Conference (DAC)
- Page Range / eLocation ID:
- 1195-1200
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/DAC18074.2021.9586166
