Neurally-Inspired Hyperdimensional Classification for Efficient and Robust Biosignal Processing
Yang Ni, Nicholas A. Lesica, Fan‐Gang Zeng, Mohsen Imani
2022Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design17 citationsDOIOpen Access PDF
Abstract
The biosignals consist of several sensors that collect time series information. Since time series contain temporal dependencies, they are difficult to process by existing machine learning algorithms. Hyper-Dimensional Computing (HDC) is introduced as a brain-inspired paradigm for lightweight time series classification. However, there are the following drawbacks with existing HDC algorithms: (1) low classification accuracy that comes from linear hyperdimensional representation, (2) lack of real-time learning support due to costly and non-hardware friendly operations, and (3) unable to build up a strong model from partially labeled data.
Topics & Concepts
BiosignalComputer scienceProcess (computing)Machine learningArtificial intelligenceRepresentation (politics)Series (stratigraphy)Time seriesData miningPattern recognition (psychology)Computer visionPolitical scienceFilter (signal processing)LawOperating systemPoliticsBiologyPaleontologyFerroelectric and Negative Capacitance DevicesAdvanced Memory and Neural ComputingNeural Networks and Reservoir Computing