Presentation
CLEAR-HD: Computationally Light and Effective Unlearning for Hyperdimensional Computing
DescriptionThe ability to selectively forget learned information--capability crucial for privacy, security, and dynamic adaptation--is unexplored in Hyperdimensional computing (HDC) systems. In this paper, we show that unlearning in HDC is challenging due to its memorization nature, making it difficult to naturally forget specific information. We then present CLEAR-HD, a light-weight and effective framework for HDC unlearning. CLEAR-HD tracks the effect of encoded vectors in the model and offsets the impact of unlearned data by appropriate substitutes. CLEAR-HD also utilizes a selective retraining to minimize accuracy loss. CLEAR-HD outperforms the baselines in unlearning quality, accuracy, and performance.
Event Type
Networking
Work-in-Progress Poster
TimeMonday, June 236:00pm - 7:00pm PDT
LocationLevel 2 Lobby