Automated quantization shrinks spike-driven language models for edge devices
QSLM framework compresses neural network models by up to 86.5% while preserving accuracy, enabling deployment on resource-constrained embedded hardware.
Source: arxiv/cs.AI · Rachmad Vidya Wicaksana Putra, Pasindu Wickramasinghe, Muhammad Shafique · open original ↗
QSLM automates quantization of spike-driven language models, reducing memory footprint by 86.5% while maintaining task accuracy.
- — Spike-driven language models reduce energy use but retain large memory footprints unsuitable for embedded devices.
- — Manual quantization is labor-intensive and does not scale across different network architectures and constraints.
- — QSLM uses tiered quantization strategy operating at global, block, and module levels to compress models.
- — Framework analyzes layer sensitivity to quantization before selecting final compression settings.
- — Achieves 86.5% memory reduction, 20% power savings, and maintains 84.4% accuracy on sentiment classification.
- — Multi-objective trade-off function balances performance and memory constraints simultaneously.
- — Tested on text generation and classification tasks with minimal accuracy loss versus baseline.
Astrobobo tool mapping
- Knowledge Capture Record the three-tier quantization strategy (global, block, module) and the multi-objective trade-off function as a reusable pattern for future compression tasks.
- Focus Brief Create a one-page summary of QSLM's layer-sensitivity analysis step; use it as a checklist when evaluating other automated compression frameworks.
- Reading Queue Queue related papers on post-training quantization and learned quantization to compare QSLM's approach against mainstream alternatives.
Frequently asked
- Spike-driven language models use neuromorphic computing principles to reduce energy consumption. Quantization compresses these models by reducing the precision of weights and activations (e.g., from 32-bit floats to 8-bit integers). QSLM automates this process by testing different quantization levels across network layers to find the best balance between model size and accuracy.
cite ▸
APA
Rachmad Vidya Wicaksana Putra, Pasindu Wickramasinghe, Muhammad Shafique. (2026, April 22). Automated quantization shrinks spike-driven language models for edge devices. Astrobobo Content Engine (rewrite of arxiv/cs.AI). https://astrobobo-content-engine.vercel.app/article/automated-quantization-shrinks-spike-driven-language-models-for-edge-devices-7ec626
MLA
Rachmad Vidya Wicaksana Putra, Pasindu Wickramasinghe, Muhammad Shafique. "Automated quantization shrinks spike-driven language models for edge devices." Astrobobo Content Engine, 22 Apr 2026, https://astrobobo-content-engine.vercel.app/article/automated-quantization-shrinks-spike-driven-language-models-for-edge-devices-7ec626. Based on "arxiv/cs.AI", https://arxiv.org/abs/2601.00679.
BibTeX
@misc{astrobobo_automated-quantization-shrinks-spike-driven-language-models-for-edge-devices-7ec626_2026,
author = {Rachmad Vidya Wicaksana Putra, Pasindu Wickramasinghe, Muhammad Shafique},
title = {Automated quantization shrinks spike-driven language models for edge devices},
year = {2026},
url = {https://astrobobo-content-engine.vercel.app/article/automated-quantization-shrinks-spike-driven-language-models-for-edge-devices-7ec626},
note = {Astrobobo rewrite of arxiv/cs.AI, https://arxiv.org/abs/2601.00679},
}