GoQuant: Geometric Orthogonal Residual Projection for Multiplier-Free Power-of-Two Transformer Quantization

GoQuant introduces Orthogonal Residual Projection (ORP), a quantization framework that enables efficient deployment of large language models on edge devices by replacing multiplication operations with bit-shifts. The approach achieves competitive performance at 3-bit precision while reducing calibration time to 15 minutes, addressing fundamental geometric limitations in power-of-two quantization.

GoQuant tackles a critical bottleneck in AI model deployment: the hardware inefficiency of dense multiply-accumulate operations that consume significant power and latency on edge devices. Traditional quantization methods struggle at ultra-low bit widths because power-of-two scaling creates non-uniform representation gaps—a geometric problem the researchers frame as 'low angular resolution.' This is particularly relevant as enterprises increasingly seek to run LLMs locally for privacy and latency reasons.

The paper emerges from a years-long tension between model compression and practical deployment. While prior work like AWQ addressed weight quantization, ORP's dual-basis projection approach solves the underlying mathematical constraint by synthesizing higher-resolution lattices using only addition and bit-shift operations—eliminating multipliers entirely from the quantization path. The 15-minute calibration time for LLaMA-2-7B is significant because it removes a major friction point in model optimization workflows.

For the AI infrastructure market, this advancement directly impacts deployment economics. Edge inference becomes more viable without expensive specialized hardware, potentially disrupting markets for quantization-specific accelerators. The 3-bit results matching 4-bit baselines suggest meaningful gains in memory bandwidth and energy efficiency—critical metrics for mobile, IoT, and robotics applications.

The hardware validation at 28nm process technology indicates the method translates beyond theory. Future work likely focuses on scaling to newer nodes and larger models, with potential applications across vision transformers and multimodal systems. Success here could accelerate the timeline for on-device AI while reducing dependence on cloud inference providers.

• →ORP replaces multiplication operations with bit-shifts through geometric projection, enabling efficient sub-4-bit quantization without hardware multipliers.
• →LLaMA-2-7B achieves 6.10 perplexity at 3-bit precision, matching conventional 4-bit methods while reducing calibration time to 15 minutes.
• →Framework addresses fundamental geometric limitations of power-of-two quantization in high-dimensional spaces through dual-basis residual projection.
• →RTL synthesis validation confirms timing bottleneck mitigation at 28nm, demonstrating hardware-software co-design feasibility.
• →Approach works across modalities without asymmetric scaling, broadening applicability beyond language models to vision transformers and multimodal systems.