Presentation
Bitwise Adaptive Early Termination in Hyperdimensional Computing Inference
DescriptionHyperdimensional computing (HDC), a powerful paradigm for cognitive tasks, often demands hypervectors of high dimensions (e.g., 10,000) to achieve competitive accuracy. However, processing such large-dimensional data poses challenges for performance and energy efficiency, particularly on resource-constrained devices. In this paper, We present a framework to terminate bit-serial HDC inference early when sufficient confidence is attained in the prediction. This approach integrates a Naive Bayes model to replace the conventional associative memory in HDC. This transformation allows for a probabilistic interpretation of the model outputs, steering away from mere similarity measures. We reduce more than 70% of bits that need to be processed while maintaining comparable accuracy across diverse benchmarks. In addition, We show the adaptability of our early termination algorithm during on-the-fly learning scenarios.
Event Type
Research Manuscript
TimeTuesday, June 254:45pm - 5:00pm PDT
Location3010, 3rd Floor
Design
Emerging Models of Computation