MAE: A 3nm 0.168mm2 576MAC Mini AutoEncoder with Line-based Depth-First Scheduling for Generative AI in Vision on Edge Devices

📋 论文概要

本文提出了一款在3nm工艺下实现的微型自动编码器（Mini AutoEncoder），面积为0.168mm²，支持576MAC运算，采用基于行的深度优先调度策略，用于边缘设备上的视觉生成式AI。解决了生成式AI在边缘端的高效推理问题，实现了低延迟和高能效。

💡 主要创新点

• 提出了基于行的深度优先调度（Line-based Depth-First Scheduling），优化了数据流和计算效率，减少了内存访问和延迟。
• 在极小的芯片面积（0.168mm²）内集成了576MAC的自动编码器，利用3nm工艺实现了高能效的生成式AI推理。

🏷 关键词

📄 原文摘要

Chia-Yuan Cheng, Hung-Wei Chih, Po-Han Chiang, Ming-Hsuan Chiang, Yuan-Jung Kuo, Yu-Wei Wu, Yi-Syuan Chen, Po-Heng Chen, Sandy Huang, Ming-En Shih, Chia-Ping Chen, Abrams Chen, ShenKai Chang, Chih-Ming Wang, Po-Yu Yeh, Jett Liu, Yung-Chang Chang, Chung-Yi Chen, Chi-Cheng Ju, CH Wang, Yucheun Kevin Jou MediaTek, Hsinchu, Taiwan Generative AI for vision has demonstrated significant potential to revolutionize user experiences through its capability to generate images with exceptional perceptual quality. The ability of generative models to handle diverse input modalities enables various image synthesis and editing applications, such as text-to-image, high-resolution image restoration, and image inpainting. Supporting these AI applications on edge devices, however, requires solutions that ensure low latency while minimizing bandwidth, power, and area at the same time. For these needs, a heterogeneous multi-core system can leverage a general neural

👥 作者与机构

Shih-Wei Hsieh, Chia-Hung Yuan, Ming-Hung Lin, Ping-Yuan Tsai, You-Yu Nian,