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Accelerating Attention with Basis Decomposition

large-language-models › model-architecture

📄 Abstract

Abstract: Attention is a core operation in large language models (LLMs) and vision-language models (VLMs). We present BD Attention (BDA), the first lossless algorithmic reformulation of attention. BDA is enabled by a simple matrix identity from Basis Decomposition (BD), which restructures multi-head projections into a compact form while preserving exact outputs. Unlike I/O-aware system optimizations such as FlashAttention, BDA provides a mathematically guaranteed acceleration that is architecture-agnostic. On DeepSeek-V2-Lite (16B, FP16), BDA requires only 4s of offline preparation with no retraining required and, on modern GPUs, achieves 32% faster key/value projections and 25% smaller weights, while increasing end-to-end perplexity (PPL) by just 0.02% (FP16) or 0.0004% (FP32), a negligible effect on model performance. These results position BDA as the first theoretically exact method for lossless attention acceleration that is complementary to existing engineering-level optimizations. Our code is available at https://github.com/abcbdf/basis-decomposition-official.

Key Contributions

Presents BD Attention (BDA), the first lossless algorithmic reformulation of attention using a matrix identity from Basis Decomposition. BDA restructures multi-head projections into a compact form, providing mathematically guaranteed acceleration without retraining, and is complementary to existing engineering optimizations.

Business Value

Enables faster training and inference of LLMs and VLMs, leading to reduced operational costs, quicker deployment cycles, and the ability to build more complex models, benefiting AI-driven applications across industries.

Paper Metadata

Innovation Type

Algorithmic

Deployment Feasibility

High, as it's an algorithmic reformulation that can be integrated into existing architectures with minimal overhead.

Limitations Addressed

The computational cost and memory footprint of the attention mechanism in LLMs and VLMs.

Performance Gains

32% faster key/value projections,25% smaller weights,Negligible increase in perplexity (0.02% FP16, 0.0004% FP32)

Technical Tags

attention accelerationbasis decompositionlossless reformulationmatrix identitymulti-head projectionsarchitecture-agnosticDeepSeek-V2-Liteperplexityengineering optimizationsLLMsVLMs

Research Topics

Machine LearningDeep LearningNatural Language ProcessingLarge Language ModelsEfficient AI

Methods & Architectures

BD Attention (BDA) algorithmBasis Decomposition (BD) matrix identityRestructuring multi-head projectionsOffline preparation BD Attention (BDA)Attention mechanismsLarge Language Models (LLMs)Vision-Language Models (VLMs)

Applications & Tasks

Natural Language Processing Computer Vision Large Language Models Vision-Language Models Attention computation bottleneckLLM/VLM efficiencyLossless acceleration Accelerating attention computation in LLMs and VLMsReducing model sizeAchieving faster inference and training

Related Fields

NLPDeep LearningEfficient AlgorithmsComputer Vision

Keywords

attentionaccelerationbasis decompositionlosslessLLMVLMtransformerefficiencydeep learningNLPmatrix identityDeepSeek

Academic Context

#Machine Learning#Deep Learning#Natural Language Processing#Large Language Models#Efficient AI

Commercial Potential

Potential Products

Optimized LLM/VLM librariesFaster AI model training services

Target Industries

TechnologySoftware DevelopmentCloud ComputingAI Research

Use Case Examples

Training large language models significantly faster.Deploying VLMs with reduced latency and memory footprint.Enabling real-time AI applications requiring fast attention computation.

Competitive Edge

Offers a theoretically exact and lossless method for attention acceleration, complementary to existing engineering-focused optimizations like FlashAttention.

Market Opportunity

Massive, tied to the growth of the LLM and VLM markets.

Revenue Models

Licensing of the BDA algorithmintegration into AI platforms.

Resource Requirements

Compute Needs

Aims to reduce compute requirements for training and inference.

Data Requirements

General datasets for LLM/VLM training.

Deployment Constraints

Requires integration into existing model architectures.

Scalability

Designed to improve the scalability of LLMs and VLMs.

Production Readiness

Maturity Level

Research

Time to Market

1-3 years

Patent Potential

High, related to the novel BD Attention algorithm and its basis decomposition method.

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