1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.AR2025★ 1 cited
Efficient Kernel Mapping and Comprehensive System Evaluation of LLM Acceleration on a CGLA
Takuto Ando, Yu Eto, Ayumu Takeuchi +1
Large Language Models (LLMs) demand substantial computational resources, resulting in high energy consumption on GPUs. To address this challenge, we focus on Coarse-Grained Reconfi…
cs.AR2025
Implementation and Evaluation of Stable Diffusion on a General-Purpose CGLA Accelerator
Takuto Ando, Yu Eto, Yasuhiko Nakashima
This paper presents the first implementation and in-depth evaluation of the primary computational kernels from the stable-diffusion.cpp image generation framework on IMAX3, a gener…
cs.AR2025
Energy-Efficient Hardware Acceleration of Whisper ASR on a CGLA
Takuto Ando, Yu Eto, Ayumu Takeuchi +1
The rise of generative AI for tasks like Automatic Speech Recognition (ASR) has created a critical energy consumption challenge. While ASICs offer high efficiency, they lack the pr…