Hypervector Design for Efficient Hyperdimensional Computing on Edge Devices

Explore hypervector design for efficient hyperdimensional computing on edge devices in this 21-minute conference talk from the tinyML Research Symposium 2021. Delve into a novel technique that minimizes hypervector dimension while maintaining accuracy and improving classifier robustness. Learn how this approach formulates hypervector design as a multi-objective optimization problem, achieving a 128x reduction in hypervector dimension and significant improvements in model size, inference time, and energy consumption. Discover the trade-offs between accuracy and robustness to noise, and gain insights into Pareto front solutions as a design parameter in hypervector design. Understand the potential applications in wearable health monitoring and neural processing, as presented by PhD student Toygun Basaklar from the University of Wisconsin-Madison.

WEARABLE HEALTH MONITORING
Hyperdimensional Computing - What is it?
Hyperdimensional Computing - Brain-Inspired?
Baseline Hyperdimensional Computing (HDC)
Motivational Example
Optimization Problem Formulation
Optimization Problem Solution
Benchmark Applications
Accuracy vs. Robustness Trade-off
Evaluation on Hardware
Accuracy Evaluation
NEURAL PROCESSING
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