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1. Seizure Detection Using Power Spectral Density via Hyperdimensional Computing

   https://doi.org/10.1109/ICASSP39728.2021.9414083  

   Ge, Lulu; Parhi, Keshab K. (June 2021, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing)

   null (Ed.)

   Hyperdimensional (HD) computing holds promise for classifying two groups of data. This paper explores seizure detection from electroencephalogram (EEG) from subjects with epilepsy using HD computing based on power spectral density (PSD) features. Publicly available intra-cranial EEG (iEEG) data collected from 4 dogs and 8 human patients in the Kaggle seizure detection contest are used in this paper. This paper explores two methods for classification. First, few ranked PSD features from small number of channels from a prior classification are used in the context of HD classification. Second, all PSD features extracted from all channels are used as features for HD classification. It is shown that for about half the subjects small number features outperform all features in the context of HD classification, and for the other half, all features outperform small number of features. HD classification achieves above 95% accuracy for six of the 12 subjects, and between 85-95% accuracy for 4 subjects. For two subjects, the classification accuracy using HD computing is not as good as classical approaches such as support vector machine classifiers. 

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2. Biological Gender Classification from fMRI via Hyperdimensional Computing

   https://doi.org/10.1109/IEEECONF53345.2021.9723179  

   Billmeyer, Ryan; Parhi, Keshab K. (October 2021, 2021 55th Asilomar Conference on Signals, Systems, and Computers)

   Hyperdimensional (HD) computing is a brain-inspired form of computing based on the manipulation of high-dimensional vectors. Offering robust data representation and relatively fast learning, HD computing is a promising candidate for energy-efficient classification of biological signals. This paper describes the application of HD computing-based machine learning to the classification of biological gender from resting-state and task functional magnetic resonance imaging (fMRI) from the publicly available Human Connectome Project (HCP). The developed HD algorithm derives predictive features through mean dynamic functional connectivity (dFC) analysis. Record encoding is employed to map features onto hyperdimensional space. Utilizing adaptive retraining techniques, the HD computing-based classifier achieves an average biological gender classification accuracy of 87%, as compared to 84% achieved by edge entropy measure. 

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3. Classification Using Hyperdimensional Computing: A Review

   https://doi.org/10.1109/mcas.2020.2988388  

   Ge, Lulu; Parhi, Keshab K. (June 2020, IEEE circuits and systems magazine)

   Hyperdimensional (HD) computing is built upon its unique data type referred to as hypervectors. The dimension of these hypervectors is typically in the range of tens of thousands. Proposed to solve cognitive tasks, HD computing aims at calculating similarity among its data. Data transformation is realized by three operations, including addition, multiplication and permutation. Its ultra-wide data representation introduces redundancy against noise. Since information is evenly distributed over every bit of the hypervectors, HD computing is inherently robust. Additionally, due to the nature of those three operations, HD computing leads to fast learning ability, high energy efficiency and acceptable accuracy in learning and classification tasks. This paper introduces the background of HD computing, and reviews the data representation, data transformation, and similarity measurement. The orthogonality in high dimensions presents opportunities for flexible computing. To balance the tradeoff between accuracy and efficiency, strategies include but are not limited to encoding, retraining, binarization and hardware acceleration. Evaluations indicate that HD computing shows great potential in addressing problems using data in the form of letters, signals and images. HD computing especially shows significant promise to replace machine learning algorithms as a light-weight classifier in the field of internet of things (IoTs). 

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4. DeepER-HD: An Error Resilient HyperDimensional Computing Framework with DNN Front-End for Feature Selection

   https://doi.org/10.1109/LATS62223.2024.10534617  

   Mejri, Mohamed; Amarnath, Chandramouli; Chatterjee, Abhijit (April 2024, IEEE)

   Brain-inspired hyperdimensional (HD) computing models mimic cognition through combinatorial bindings of biological neuronal data represented by high-dimensional vectors and related operations. However, the efficacy of HD computing depends strongly on input signal and data features used to realize such bindings. In this paper, we propose a new HD-computing framework based on a co-trainable DNN-based feature extractor pre-processor and a hyperdimensional computing system. When trained with restrictions on the ranges of hypervector elements for resilience to memory access errors, the framework achieves up to 135% accuracy improvement over baseline HD-computing for error-free operation and up to three orders of magnitude improvement in error resilience compared to the state-of-the-art. Results for a range of applications from image classification, face recognition, human activity recognition and medical diagnosis are presented and demonstrate the viability of the proposed ideas. 

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5. QuantHD: A Quantization Framework for Hyperdimensional Computing

   https://doi.org/10.1109/TCAD.2019.2954472  

   Imani, M; Bosch, S; Datta, S; Ramakrishna, S; Salamat, S; Rabaey, J; Rosing, T. (November 2019, IEEE transactions on computeraided design of integrated circuits and systems)

   Brain-inspired Hyperdimensional (HD) computing models cognition by exploiting properties of high dimensional statistics– high-dimensional vectors, instead of working with numeric values used in contemporary processors. A fundamental weakness of existing HD computing algorithms is that they require to use floating point models in order to provide acceptable accuracy on realistic classification problems. However, working with floating point values significantly increases the HD computation cost. To address this issue, we proposed QuantHD, a novel framework for quantization of HD computing model during training. QuantHD enables HD computing to work with a low-cost quantized model (binary or ternary model) while providing a similar accuracy as the floating point model. We accordingly propose an FPGA implementation which accelerates HD computing in both training and inference phases. We evaluate QuantHD accuracy and efficiency on various real-world applications, and observe that QuantHD can achieve on average 17.2% accuracy improvement as compared to the existing binarized HD computing algorithms which provide a similar computation cost. In terms of efficiency, QuantHD FPGA implementation can achieve on average 42.3× and 4.7× (34.1× and 4.1×) energy efficiency improvement and speedup during inference (training) as compared to the state-of-the-art HD computing algorithms. 

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