5 papers
ZIPBrain: Can EEG Foundation Models Be Faster, Locally Deployable, but Accurate?
Lingwei Li, Yirong Kan, Peng Chen +3
This work investigates whether Electroencephalograph (EEG) foundation models (EFMs) can be made faster and locally deployable without sacrificing accuracy. EEG foundation models ar…
Design and Evaluation of Energy-Efficient Whisper Dot-Product Kernel Offloading on a CGLA Architecture
Takuto Ando, Yu Eto, Ayumu Takeuchi +1
In this paper, we implement and evaluate Whisper dot-product kernel offloading on IMAX, a programmable Coarse-Grained Linear Arrays (CGLAs) architecture. Whisper-tiny.en profiling…
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…
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…
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…