Rethinking Attention Output Projection: Structured Hadamard Transforms for Efficient Transformers
Rethinking Attention Output Projection: Structured Hadamard Transforms for Efficient Transformers explores Replace the dense output projection in multi-head attention with a structured Hadamard transform for efficient Transformers, reducing parameters and improving throughput.. Commercial viability score: 7/10 in Transformer Optimization.
BUILDER'S SANDBOX
Build This Paper
Use an AI coding agent to implement this research.
OpenAI CodexAI Agent
Lightweight coding agent in your terminal.
Claude CodeAI Agent
Agentic coding tool for terminal workflows.
AntiGravity IDEScaffolding
AI agent mindset installer and workflow scaffolder.
CursorIDE
AI-first code editor built on VS Code.
VS CodeIDE
Free, open-source editor by Microsoft.
Recommended Stack
Startup Essentials
Estimated $9K - $13K over 6-10 weeks.
See exactly what it costs to build this -- with 3 comparable funded startups.
7-day free trial. Cancel anytime.
Discover the researchers behind this paper and find similar experts.
7-day free trial. Cancel anytime.
Related Papers
- Builds On ThisHLA: Hadamard Linear AttentionViability: 4.0
- Builds On ThisOn the Computational Hardness of TransformersViability: 2.0
- Builds On ThisAffine-Scaled Attention: Towards Flexible and Stable Transformer AttentionViability: 2.0
- Builds On ThisEfficient Reasoning at Fixed Test-Time Cost via Length-Aware Attention Priors and Gain-Aware TrainingViability: 2.0
- Builds On ThisAttention ResidualsViability: 3.0