[論文レビュー] Sensitivity-Guided Framework for Pruned and Quantized Reservoir Computing Accelerators

Atousa Jafari, Mahdi Taheri+3 more|arXiv (Cornell University)|Feb 24, 2026

Neural Networks and Reservoir Computing被引用数 0

ひとこと要約

A sensitivity-guided compression framework for reservoir computing on FPGA that combines quantization and pruning to explore design trade-offs between accuracy and hardware efficiency, enabling end-to-end accelerator synthesis and outperforming traditional pruning methods.

ABSTRACT

This paper presents a compression framework for Reservoir Computing that enables systematic design-space exploration of trade-offs among quantization levels, pruning rates, model accuracy, and hardware efficiency. The proposed approach leverages a sensitivity-based pruning mechanism to identify and remove less critical quantized weights with minimal impact on model accuracy, thereby reducing computational overhead while preserving accuracy. We perform an extensive trade-off analysis to validate the effectiveness of the proposed framework and the impact of pruning and quantization on model performance and hardware parameters. For this evaluation, we employ three time-series datasets, including both classification and regression tasks. Experimental results across selected benchmarks demonstrate that our proposed approach maintains high accuracy while substantially improving computational and resource efficiency in FPGA-based implementations, with variations observed across different configurations and time series applications. For instance, for the MELBOEN dataset, an accelerator quantized to 4-bit at a 15\% pruning rate reduces resource utilization by 1.2\% and the Power Delay Product (PDP) by 50.8\% compared to an unpruned model, without any noticeable degradation in accuracy.

研究の動機と目的

• Motivate scalable deployment of Reservoir Computing on resource-constrained edge devices by reducing model size and compute without sacrificing accuracy.
• Develop a sensitivity-guided pruning mechanism that identifies and removes less critical quantized weights to minimize accuracy loss.
• Enable end-to-end hardware mapping of compressed RC models onto FPGAs to study hardware metrics like resource usage, latency, throughput, and power.
• Provide a design-space exploration framework to quantify trade-offs among quantization levels, pruning rates, model accuracy, and hardware parameters.

提案手法

• Introduce a sensitivity-based analysis that evaluates the functional impact of each weight by simulating bit-flips on quantized weights and measuring output performance deviation.
• Quantize reservoir weights with a linear quantization scheme and employ a hardware-friendly streamline approach to map activations to integer steps.
• Compute a sensitivity score for each weight as the average performance deviation across all bit positions, and prune the lowest-sensitivity weights according to a given pruning rate.
• Use a four-stage accelerator synthesis flow: dataset/configuration, hyperparameter optimization, quantization and pruning, followed by RTL generation and FPGA synthesis.
• Implement an end-to-end design-space exploration algorithm that iterates over quantization levels and pruning rates to produce multiple accelerator configurations for hardware realization.

実験結果

リサーチクエスチョン

• RQ1 How does sensitivity-guided pruning compare to correlation-based pruning methods in preserving RC accuracy under quantization?
• RQ2 What are the hardware-performance implications (LUT/FF usage, latency, throughput, PDP) of different quantization/pruning configurations on FPGA-based RC accelerators?
• RQ3 Can the proposed framework identify optimal quantization-pruning configurations that balance accuracy with resource and energy efficiency across diverse time-series tasks?
• RQ4 Does the sensitivity-guided approach require retraining after pruning, and how does it affect model regularization and generalization?

主な発見

• Sensitivity-guided pruning consistently underperforms less sophisticated pruning methods in accuracy/ RMSE across 4-, 6-, and 8-bit quantizations and pruning rates, with a few exceptions.
• On MELBORN classification, 4-bit quantization at 15% pruning achieves 50.88% PDP savings with 1.26% resource saving while preserving accuracy.
• Across datasets, sensitivity-guided pruning yields smaller accuracy/ RMSE degradation and slower performance decline than MI, random, Spearman, PCA, and Lasso pruning.
• Hardware results show substantial PDP reductions and maintained or improved throughput with aggressive pruning, due to a direct-logic FPGA mapping that avoids memory bottlenecks.
• The framework enables design-space exploration of trade-offs among bit-width, pruning rate, and hardware metrics, facilitating optimized RC accelerators for different tasks.