DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing
Journal:
arXiv
Published Date:
May 8, 2025
Abstract
Hyperdimensional Computing (HDC) is emerging as a promising approach for edge
AI, offering a balance between accuracy and efficiency. However, current
HDC-based applications often rely on high-precision models and/or encoding
matrices to achieve competitive performance, which imposes significant
computational and memory demands, especially for ultra-low power devices. While
recent efforts use techniques like precision reduction and pruning to increase
the efficiency, most require retraining to maintain performance, making them
expensive and impractical. To address this issue, we propose a novel Post
Training Compression algorithm, Decomposition-Pruning-Quantization (DPQ-HD),
which aims at compressing the end-to-end HDC system, achieving near floating
point performance without the need of retraining. DPQ-HD reduces computational
and memory overhead by uniquely combining the above three compression
techniques and efficiently adapts to hardware constraints. Additionally, we
introduce an energy-efficient inference approach that progressively evaluates
similarity scores such as cosine similarity and performs early exit to reduce
the computation, accelerating prediction inference while maintaining accuracy.
We demonstrate that DPQ-HD achieves up to 20-100x reduction in memory for image
and graph classification tasks with only a 1-2% drop in accuracy compared to
uncompressed workloads. Lastly, we show that DPQ-HD outperforms the existing
post-training compression methods and performs better or at par with
retraining-based state-of-the-art techniques, requiring significantly less
overall optimization time (up to 100x) and faster inference (up to 56x) on a
microcontroller
