A 1.42TOPS/W Deep Convolutional Neural Network Recognition Processor for Intelligent IoE Systems

📋 论文概要

该论文提出了一种用于智能物联网系统的高能效深度卷积神经网络识别处理器，解决了传统识别处理器只能加速特定应用的问题，实现了1.42TOPS/W的能效。

💡 主要创新点

• 提出了一种支持多种深度卷积神经网络架构的通用处理器架构，可灵活应用于图像和音频识别。
• 通过优化数据流和计算单元，实现了高能效的卷积计算，能效达到1.42TOPS/W。

🏷 关键词

📄 原文摘要

Internet-ofEverything (IoE) devices to data center servers for intelligent recognition processes is impractical for energy reasons, requiring in-situ processing of such data. However, algorithms accelerated by previous recognition processors [1, 2] are limited to specific applications, therefore, each IoE device may require an application-specific accelerator. On the other hand, deep convolutional neural networks (CNNs) [3] are a promising machine-learning approach, showing stateof-the-art recognition accuracy in a wide variety of applications, including both image and audio recognition. This makes CNNs a suitable candidate for a universal recognition platform for IoE devices, as described in Fig. 14.6.1. Due to the computational complexity and significant memory requirements of CNNs, a

👥 作者与机构

Jaehyeong Sim, Jun-Seok Park, Minhye Kim, Dongmyung Bae,

Yeongjae Choi, Lee-Sup Kim KAIST, Daejeon, Korea Transmitting massive amounts of image and audio data acquired by