Aug 2026· Symmetry· Vol 18, pp. 1387· 0 citations· 25 references
TL;DR
This study proposes a collaborative multi-compression framework for lightweight deployment that combines adaptive importance-aware structured pruning, mixed-precision quantization, and quantization-aware multi-stage knowledge distillation on LiteKWS-Net.
Abstract
To address the large model size, high computational cost, and limited deployment resources of keyword spotting models on edge platforms, this study proposes a collaborative multi-compression framework for lightweight deployment. Built on LiteKWS-Net, an attention-enhanced 2-D convolutional backbone, the framework combines adaptive importance-aware structured pruning, mixed-precision quantization, and quantization-aware multi-stage knowledge distillation. The retrained teacher reaches 97.90% (mean, 100,813 parameters, 0.385 MiB). MPDQ reaches 95.53 ± 1.16% at 8.27× theoretical weight compression. AIASP reaches 97.59% at a 30% target and 43.9% realized sparsity. The final joint model reaches 96.82% and, under ideal packed sparse mixed-precision storage, has a 51.55× theoretical weight-compression factor relative to the FP32 teacher; sparse-index overhead is excluded. On a Jetson Nano, the TensorRT FP16 network-body benchmark reports 2.86 ms latency and 0.69 mJ per inference.
The proposed hybrid pipeline includes structured pruning, INT8 quantization and task-specific knowledge distillation, which is benchmarked against standalone methods and reinforces the idea of upper bound projection based approach for accuracy-oriented, multi-level compression.
Krishna Kumar Tiwari, Komal Tahiliani, Uma Shankar Birthare et al.· International journal of com...· 0 citations
Experimental results demonstrate that effective compression significantly reduces model size and computational cost with minimal performance loss, highlighting the importance of compression-aware design and concluding as a valuable reference for building efficient and scalable AI systems.
Daniel Rodríguez· International Journal of App...· 0 citations
Mixture-of-Experts (MoE) LLMs scale model capacity efficiently through sparse activation, but their large expert parameter footprint, routing imbalance, and long-context KV-cache growth make deployment difficult on commodity hardware. Practical deployment often requires stacking multiple compression techniques: expert pruning removes redundant experts, weight quantization lowers model memory footprint, and KV-cache compression reduces long-context memory pressure. However, these techniques are typically evaluated in isolation, leaving open how they interact when applied together in realistic deployment pipelines. In this work, we present MoEXBench, a systematic benchmark for evaluating composable MoE compression as an end-to-end deployment workflow. MoEXBench studies 10 MoE models ranging from 30B to 235B total parameters across standard-attention, hybrid linear-attention, and sliding window attention architectures. It evaluates 20%-50% expert pruning rates, 1 to 16 bit weight-quantization schemes, and multiple KV-cache precision settings, applied both individually and in combination. MoEXBench introduces an eight-module evaluation suite that jointly measures composable-compression quality, workload and architecture robustness, pruning/quantization/KV cache sensitivity, and deployment efficiency on commodity hardware. Our results reveal non-trivial interactions among compression methods: composable compression cannot be predicted from standalone techniques, compression rate alone does not reliably predict quality loss or runtime gain, expert pruning is the dominant degradation source, and average quality can hide workload and architecture-specific failures. By releasing normalized module scores, compressed artifacts, and reproducible scripts, MoEXBench enables practical accuracy-memory-latency comparison across MoE families and hardware backends.
A. Benazir, Chen Chen, Rongxiao Qu et al.· 0 citations
REP-LIE leverages the gradients of LoRA low-rank matrices to estimate the importance of weights without requiring full gradient computation, and a stability score is introduced, serving as the basis for iterative pruning of unimportant model parameters.
Peng Liu, Hui-Bing Zeng, Yi-Qun Zhang et al.· IEEE Transactions on Emergin...· 0 citations
Findings confirm that combining complementary compression strategies yields substantially better performance-efficiency trade-offs than any single technique applied in isolation.
Upma Sharma Archana· International Journal of Res...· 0 citations
An Adaptive Depth Sparse Framework (AdaDSF) that converts off-the-shelf pre-trained LLMs into depth-sparse models without full retraining and consistently yields smaller accuracy degradation than strong baselines including MoD, D-LLM, and DLO.
Yi-Da Wu, Xiang Wang, Kejie Zhao et al.· International Conference on...· 0 citations
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