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ATFlash: Per-RoPE-Wavelength Attention Windows for Compute/Memory-Efficient LLM Inference
Shun-ichiro Hayashi, Daichi Mukunoki, Tetsuya Hoshino +1
The attention score with rotary position embeddings (RoPE) decomposes exactly into a sum over its 2D-rotation frequency pairs, and each pair's wavelength limits how far it can disc…
Formulation-Level Auto-Tuning for QUBO-Based Machine Learning: A Case Study Across Multiple Quantum-Inspired Annealers
Naoya Mizuki, Takahiro Katagiri, Daichi Mukunoki +1
This paper presents an Optuna-based formulation-level auto-tuning framework for support vector machines (SVMs) implemented on multiple quantum-inspired annealers. In an annealing-b…
Improving HPC Code Generation Capability of LLMs via Online Reinforcement Learning with Real-Machine Benchmark Rewards
Ryo Mikasa, Shun-ichiro Hayashi, Daichi Mukunoki +2
Large language models (LLMs) have demonstrated strong code generation capabilities, yet the runtime performance of generated code is not guaranteed, and there have been few attempt…
Performance Evaluation of General Purpose Large Language Models for Basic Linear Algebra Subprograms Code Generation
Daichi Mukunoki, Shun-ichiro Hayashi, Tetsuya Hoshino +1
Generative AI technology based on Large Language Models (LLM) has been developed and applied to assist or automatically generate program codes. In this paper, we evaluate the capab…