RoPE Hardware Accelerator Based on Uniformly Distributed CORDIC: 62% Power Reduction for Edge LLM Inference

The team from the International Institute of Information Technology Bangalore (IIIT Bangalore) proposes two Uniformly Distributed CORDIC (UD-CORDIC) architectures: Binary and CSD. These eliminate the Z-path control logic of traditional CORDIC. In a 45nm CMOS process, they achieve up to 64.5% power reduction and 31.4% area reduction, and are verified to be applicable to mainstream models like LLaMA-2, Mistral, and Gemma-2. The research source is GitHub, and the release date is June 2026.

Rotary Position Encoding (RoPE) is a core position-aware mechanism in modern Transformer architectures and is widely adopted by mainstream open-source large models. However, its hardware implementation faces many challenges: huge lookup table (LUT) overhead, intensive floating-point operations, high memory bandwidth pressure, and prominent power consumption issues—especially when deployed on edge devices, the energy consumption proportion cannot be ignored.

The core insight of UD-CORDIC is to leverage the uniform distribution characteristic of rotation angles, directly extract the rotation direction from the binary representation of angles, eliminate the Z-path control logic of traditional CORDIC, and achieve an open-loop architecture and pipeline-friendly design. The team proposes two optimized architectures: Binary UD-CORDIC (minimal hardware, replacing multipliers with shifters) and CSD UD-CORDIC (merging consecutive stages, halving the number of stages, reducing power consumption and area).

The Q(1,F) fixed-point representation (1 integer bit + F fractional bits) is used to cover the numerical range of RoPE computation. Precision scanning experiments show that when F≥7, the model perplexity degradation is less than 1%. F=8 is recommended as the default configuration to balance hardware efficiency and model precision.

In the 45nm CMOS process, Binary UD-CORDIC achieves 12.6% area reduction and 33%-37% power reduction; CSD UD-CORDIC achieves 27.1%-31.4% area reduction and 62.3%-64.5% power reduction, effectively extending the battery life of edge devices and alleviating heat dissipation pressure.

This research provides directly integrable RTL code, verified quantization strategies, and trade-off data, suitable for scenarios such as smartphone NPUs and autonomous driving chips. Future directions include exploring mixed-precision support, sparsity utilization, multi-core expansion, and migration to advanced processes.

The UD-CORDIC RoPE accelerator achieves over 60% power reduction and 30% area reduction through algorithm-architecture co-design, providing an efficient architectural reference for edge LLM inference systems and facilitating the migration of large models to the edge.