LASER introduces curvature-weighted SVD from second-order loss approximation and loss-aware rank allocation to compress VLMs, reporting over 2.3x decoding speedup under low-precision settings.
Svd-llm v2: Optimizing singular value truncation for large language model compression.arXiv preprint arXiv:2503.12340
6 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
DREAM-S combines neural architecture search, target-aware supernet training, and attention-entropy-guided distillation to accelerate speculative decoding in VLMs, reporting up to 3.85x speedup over standard methods.
A3 splits Transformer layers into QK, OV, and MLP components and derives analytical low-rank approximations that reduce hidden dimensions while minimizing each component's functional loss, yielding better perplexity than prior low-rank methods on LLaMA models.
AIR augments activation-aware SVD compression of LLMs with an influence metric and a closed-form ALS update, claiming >18% perplexity improvement at 60% parameter retention and 90% less calibration data than SVD-LLM(W).
Unifying cross-layer SVD compression for LLMs improves weight reconstruction error by up to 46% on Pythia models but causes severe degradation in perplexity and accuracy due to residual stream decoupling.
NeuronMLP applies SVD-based compression and Trainium-specific tiling and caching to MLP layers, delivering 1.35x kernel speedup and 1.21x end-to-end inference speedup at 0.05 compression ratio versus AWS NKI baseline.
citing papers explorer
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LASER: Loss-Aware Singular-value Decomposition and Rank Allocation for Efficient Low-Precision Vision-Language Models
LASER introduces curvature-weighted SVD from second-order loss approximation and loss-aware rank allocation to compress VLMs, reporting over 2.3x decoding speedup under low-precision settings.
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DREAM-S: Speculative Decoding with Searchable Drafting and Target-Aware Refinement for Multimodal Generation
DREAM-S combines neural architecture search, target-aware supernet training, and attention-entropy-guided distillation to accelerate speculative decoding in VLMs, reporting up to 3.85x speedup over standard methods.
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A3 : an Analytical Low-Rank Approximation Framework for Attention
A3 splits Transformer layers into QK, OV, and MLP components and derives analytical low-rank approximations that reduce hidden dimensions while minimizing each component's functional loss, yielding better perplexity than prior low-rank methods on LLaMA models.
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Activation- and Influence-Aware Ranks (AIR): Function-Preserving SVD Compression for LLMs
AIR augments activation-aware SVD compression of LLMs with an influence metric and a closed-form ALS update, claiming >18% perplexity improvement at 60% parameter retention and 90% less calibration data than SVD-LLM(W).
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Cross-Layer Subspace Coupling for LLM Compression: A Unifying Framework and Its Empirical Limits
Unifying cross-layer SVD compression for LLMs improves weight reconstruction error by up to 46% on Pythia models but causes severe degradation in perplexity and accuracy due to residual stream decoupling.
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NeuronMLP: Efficient LLM Inference via Singular Value Decomposition Compression and Tiling on AWS Trainium
NeuronMLP applies SVD-based compression and Trainium-specific tiling and caching to MLP layers, delivering 1.35x kernel speedup and 1.21x end-to-end inference speedup at 0.05 compression ratio versus AWS NKI baseline.