Hyperdimensional vector processing is a nascent computing approach that mimics the brain structure and offers lightweight, robust, and efficient hardware solutions for different learning and cognitive tasks. For image recognition and classification, hyperdimensional computing (HDC) utilizes the intensity values of captured images and the positions of image pixels. Traditional HDC systems represent the intensity and positions with binary hypervectors of 1K–10K dimensions. The intensity hypervectors are cross-correlated for closer values and uncorrelated for distant values in the intensity range. The position hypervectors are pseudo-random binary vectors generated iteratively for the best classification performance. In this study, we propose a radically new approach for encoding image data in HDC systems. Position hypervectors are no longer needed by encoding pixel intensities using a deterministic approach based on quasi-random sequences. The proposed approach significantly reduces the number of operations by eliminating the position hypervectors and the multiplication operations in the HDC system. Additionally, we suggest a hybrid technique for generating hypervectors by combining two deterministic sequences, achieving higher classification accuracy. Our experimental results show up to 102× reduction in runtime and significant memory-usage savings with improved accuracy compared to a baseline HDC system with conventional hypervector encoding.
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Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices
Hyperdimensional computing (HDC) has emerged as a new light-weight learning algorithm with smaller computation and energy requirements compared to conventional techniques. In HDC, data points are represented by high dimensional vectors (hypervectors), which are mapped to high dimensional space (hyperspace). Typically, a large hypervector dimension (≥1000) is required to achieve accuracies comparable to conventional alternatives. However, unnecessarily large hypervectors increase hardware and energy costs, which can undermine their benefits. This paper presents a technique to minimize the hypervector dimension while maintaining the accuracy and improving the robustness of the classifier. To this end, we formulate hypervector design as a multi-objective optimization problem for the first time in the literature. The proposed approach decreases the hypervector dimension by more than 128× while maintaining or increasing the accuracy achieved by conventional HDC. Experiments on a commercial hardware platform show that the proposed approach achieves more than two orders of magnitude reduction in model size, inference time, and energy consumption. We also demonstrate the trade-off between accuracy and robustness to noise and provide Pareto front solutions as a design parameter in our hypervector design.
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- Award ID(s):
- 2114499
- PAR ID:
- 10334215
- Date Published:
- Journal Name:
- tinyML 2021 Research Symposium
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Hyperdimensional computing (HDC) is a novel computational paradigm that operates on long-dimensional vectors known as hypervectors. The hypervectors are constructed as long bit-streams and form the basic building blocks of HDC systems. In HDC, hypervectors are generated from scalar values without considering bit significance. HDC is efficient and robust for various data processing applications, especially computer vision tasks. To construct HDC models for vision applications, the current state-of-the-art practice utilizes two parameters for data encoding: pixel intensity and pixel position. However, the intensity and position information embedded in high-dimensional vectors are generally not generated dynamically in the HDC models. Consequently, the optimal design of hypervectors with high model accuracy requires powerful computing platforms for training. A more efficient approach is to generate hypervectors dynamically during the training phase. To this aim, this work uses low-discrepancy sequences to generate intensity hypervectors, while avoiding position hypervectors. Doing so eliminates the multiplication step in vector encoding, resulting in a power-efficient HDC system. For the first time in the literature, our proposed approach employs lightweight vector generators utilizing unary bit-streams for efficient encoding of data instead of using conventional comparator-based generators.more » « less
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Hyperdimensional computing (HDC) is a novel computational paradigm that operates on long-dimensional vectors known as hypervectors. The hypervectors are constructed as long bit-streams and form the basic building blocks of HDC systems. In HDC, hypervectors are generated from scalar values without considering bit significance. HDC is efficient and robust for various data processing applications, especially computer vision tasks. To construct HDC models for vision applications, the current state-of-the-art practice utilizes two parameters for data encoding: pixel intensity and pixel position. However, the intensity and position information embedded in high-dimensional vectors are generally not generated dynamically in the HDC models. Consequently, the optimal design of hypervectors with high model accuracy requires powerful computing platforms for training. A more efficient approach is to generate hypervectors dynamically during the training phase. To this aim, this work uses low-discrepancy sequences to generate intensity hypervectors, while avoiding position hypervectors. Doing so eliminates the multiplication step in vector encoding, resulting in a power-efficient HDC system. For the first time in the literature, our proposed approach employs lightweight vector generators utilizing unary bit-streams for efficient encoding of data instead of using conventional comparator-based generators.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.48550
