OrpQuant: Geometric Orthogonal Residual Projection for Multiplier-Free Power-of-Two Transformer Quantization
arXiv:2605.26092v1 Announce Type: new Abstract: The deployment of Large Language Models (LLMs) and Vision Transformers (ViTs) on edge devices is significantly constrained by memory limitations and the critical timing bottlenecks introduced by dense Multiply-Accumulate (MAC) arrays. In the ultra-low bit regime, logarithmic Power-of-Two (PoT) quantization provides a hardware-efficient alternative by replacing MAC operations with bit-shifts. However, the non-uniform exponential lattice is inherently limited by a \textbf{Low Angular Resolution Regime}, a structural flaw that becomes particularly p
The increasing scale and resource demands of large language models necessitate innovation in hardware efficiency, especially for edge deployment.
This development addresses critical memory and computational bottlenecks, paving the way for wider deployment of advanced AI on power-constrained devices.
The ability to perform power-of-two quantization more effectively improves AI model efficiency, potentially reducing hardware requirements for advanced AI.
- · Edge AI device manufacturers
- · AI model developers
- · Cloud computing providers
- · Consumers of AI-powered devices
More powerful AI models can be deployed on embedded systems and mobile devices.
This could accelerate the development and adoption of AI applications requiring low latency and on-device processing.
Reduced computational overhead contributes to the overall availability and accessibility of advanced AI, potentially democratizing its use.
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Read at arXiv cs.LG