DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing (2505.05413v1)

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

Analyzing DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing

The paper "DPQ-HD: Post-Training Compression for Ultra-Low Power Hyperdimensional Computing" addresses a salient issue in the domain of Hyperdimensional Computing (HDC): optimizing computational and memory efficiency for edge AI deployments without significantly compromising model accuracy. The authors propose a novel framework, DPQ-HD, which utilizes a synergistic combination of decomposition, pruning, and quantization techniques to achieve significant reductions in system complexity and power consumption.

At its core, HDC is a promising brain-inspired machine learning paradigm, conducive to edge computing due to the inherent parallelism and low complexity of its operations. However, the main challenge in deploying these systems lies in the substantial resource demand posed by the usual high-precision projections and encoding matrices used to retain competitive performance accuracy.

DPQ-HD circumvents the need for exhaustive retraining—proposed in previous solutions like precision reduction or pruning—offering a more computationally efficient approach. The framework exploits the characteristics of low-rank matrix decomposition to replace expensive projections, conserves resources through strategic pruning of non-essential dimensions, and employs quantization to further compress data size without losing critical information.

Experimental results presented in the paper demonstrate the profound impact of DPQ-HD in real-world scenarios. The authors report up to 20-100x reduction in memory usage with a minor accuracy compromise of just 1-2% across various challenging datasets and tasks, including image and graph classification on ultra-low power devices. Particularly notable is the method's effectiveness when compared to prevalent post-training and retraining-based compression methods, demonstrating significant improvements in optimization time (up to 100x faster) and microcontroller inference speeds (up to 56x faster). This is achieved through careful calibration of the algorithm's parameters, such as decomposition rank and pruning ratios, to cater to the specific characteristics of each dataset.

The inclusion of an adaptive online inference strategy is a distinguishing feature of the framework. This strategy involves progressively evaluating and potentially exiting inference early based on dynamic computations of similarity thresholds—further accelerating prediction inference while preserving accuracy. This approach ensures even ultra-low-power microcontroller units can efficiently perform computations typically constrained by power and memory limitations.

Implications of this research are profound for the continued advancement of edge AI, where balancing power efficiency with computational capability remains a pivotal challenge. DPQ-HD not only provides a comprehensive solution to optimize HDC systems but also paves the way for more widespread adoption of AI in embedded systems, IoT devices, and remote sensors with limited resources. Future developments in combining decomposition, pruning, and quantization might further harness other optimizations, potentially leading to more sophisticated, efficient models capable of handling even larger datasets and more complex tasks.

The proposed DPQ-HD offers a unique, efficient, and comprehensive framework that stands out in the field of edge AI, contributing significantly to enabling advanced computing capabilities in constrained environments. Its ability to achieve remarkable reductions in memory and energy requirements, while maintaining a competitive edge in accuracy, represents a meaningful step toward more sustainable AI solutions.