🔋 Addition is All You Need for Energy-efficient Language Models

This research paper introduces a novel algorithm called Linear-Complexity Multiplication (L-Mul) that aims to make language models more energy-efficient. L-Mul replaces computationally expensive floating-point multiplications with integer addition operations, significantly reducing energy consumption. The authors demonstrate that L-Mul achieves high precision, even surpassing 8-bit floating-point multiplications in certain cases. They evaluate L-Mul on various benchmarks, including natural language, vision, and mathematics tasks, showing that L-Mul can be effectively implemented in attention mechanisms without compromising performance, leading to significant energy savings in model deployment. The authors conclude that L-Mul holds great potential for creating more energy-efficient and cost-effective AI systems.

📎 Link to paper