Year: 2025
Type: conference-paper
Abstract: Hyperdimensional Computing (HDC) is a brain-inspired, lightweight computing paradigm that has shown great potential for inference on the edge and on emerging hardware technologies, achieving state-of-the-art accuracy on certain classification tasks. HDC classifiers are inherently error resilient and support early termination of inference to approximate classification results. Practitioners have developed heuristic methods to terminate inference early for individual inputs, reducing the computation of inference at the cost of accuracy. These techniques lack statistical guarantees and may unacceptably degrade classification accuracy or terminate inference later than is needed to obtain an accuracy result.
Authors: Pu (Luke) Yi, Yifan Yang, Chae Young Lee, Sara Achour
Institution: Stanford University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Funder: Stanford SystemX Alliance
OpenAlex ID: W4407217583