QuBD extends algorithmic complexity estimation to quantized DNN weights, revealing that complexity decreases during learning, increases with overfitting, follows grokking patterns, and correlates with generalization.
arXiv preprint arXiv:2409.17066 , year=
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LiftQuant enables continuous bit-width LLM quantization via dimensional lifting and projection from a 1-bit lattice, allowing 2.4-bit compression of 70B models that outperforms fixed 2-bit baselines on identical hardware.
LBLLM achieves better accuracy than prior binarization methods for LLMs by decoupling weight and activation quantization through initialization, layer-wise distillation, and learnable activation scaling.
BTC-LLM uses a binary codebook for pattern clustering and a learnable transformation to achieve 0.7-1.11 bit LLM quantization while limiting accuracy loss to a few percent on LLaMA and Qwen models.
Presents LLaVA-AlignedVQ, an edge-cloud VQA system with AlignedVQ that delivers 1365x feature compression, 96.8% lower transmission than JPEG90, 2-15x speedup, and accuracy within -2.23% to +1.6% of the baseline across eight datasets.
EVA is a vector-quantization hardware architecture that transforms LLM decoding from GEMV to GEMM via direct codebook dot products and conflict-free output buffering, claiming up to 11.17x speedup over prior lookup designs.
MGVQ introduces sensitivity-aware structured mixed-precision VQ and gradient-aware second-order error compensation using Kronecker and Block-LDL decompositions, reporting up to 4.9 point gains over prior methods at 2-bit on models like InternVL2-26B.
BPDQ creates variable quantization grids from bit-planes and scalar coefficients, refined iteratively with second-order data to minimize output error, enabling 2-bit serving of Qwen2.5-72B on one RTX 3090 at 83.85% GSM8K accuracy.
citing papers explorer
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Characterizing Learning in Deep Neural Networks using Tractable Algorithmic Complexity Analysis
QuBD extends algorithmic complexity estimation to quantized DNN weights, revealing that complexity decreases during learning, increases with overfitting, follows grokking patterns, and correlates with generalization.
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LiftQuant: Continuous Bit-Width LLM via Dimensional Lifting and Projection
LiftQuant enables continuous bit-width LLM quantization via dimensional lifting and projection from a 1-bit lattice, allowing 2.4-bit compression of 70B models that outperforms fixed 2-bit baselines on identical hardware.
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LBLLM: Lightweight Binarization of Large Language Models via Three-Stage Distillation
LBLLM achieves better accuracy than prior binarization methods for LLMs by decoupling weight and activation quantization through initialization, layer-wise distillation, and learnable activation scaling.
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BTC-LLM: Efficient Sub-1-Bit LLM Quantization via Learnable Transformation and Binary Codebook
BTC-LLM uses a binary codebook for pattern clustering and a learnable transformation to achieve 0.7-1.11 bit LLM quantization while limiting accuracy loss to a few percent on LLaMA and Qwen models.
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Aligned Vector Quantization for Edge-Cloud Collabrative Vision-Language Models
Presents LLaVA-AlignedVQ, an edge-cloud VQA system with AlignedVQ that delivers 1365x feature compression, 96.8% lower transmission than JPEG90, 2-15x speedup, and accuracy within -2.23% to +1.6% of the baseline across eight datasets.
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EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture
EVA is a vector-quantization hardware architecture that transforms LLM decoding from GEMV to GEMM via direct codebook dot products and conflict-free output buffering, claiming up to 11.17x speedup over prior lookup designs.
-
MGVQ: Synergizing Multi-dimensional Sensitivity-Aware and Gradient-Hessian Fusion for Vector Quantization
MGVQ introduces sensitivity-aware structured mixed-precision VQ and gradient-aware second-order error compensation using Kronecker and Block-LDL decompositions, reporting up to 4.9 point gains over prior methods at 2-bit on models like InternVL2-26B.
-
BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models
BPDQ creates variable quantization grids from bit-planes and scalar coefficients, refined iteratively with second-order data to minimize output error, enabling 2-bit serving of Qwen2.5-72B on one RTX 3090 at 83.85% GSM8K accuracy.