Sensitivity-Guided Framework for Pruned and Quantized Reservoir Computing Accelerators
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866918380571721728 |
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| author | Jafari, Atousa Taheri, Mahdi Mohammadi, Hassan Ghasemzadeh Herglotz, Christian Platzner, Marco |
| author_facet | Jafari, Atousa Taheri, Mahdi Mohammadi, Hassan Ghasemzadeh Herglotz, Christian Platzner, Marco |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_08737 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Sensitivity-Guided Framework for Pruned and Quantized Reservoir Computing Accelerators Jafari, Atousa Taheri, Mahdi Mohammadi, Hassan Ghasemzadeh Herglotz, Christian Platzner, Marco Hardware Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning 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. |
| title | Sensitivity-Guided Framework for Pruned and Quantized Reservoir Computing Accelerators |
| topic | Hardware Architecture Artificial Intelligence Distributed, Parallel, and Cluster Computing Machine Learning |
| url | https://arxiv.org/abs/2603.08737 |