A 646GOPS/W Multi-Classifier Many-Core Processor with Cortex-Like Architecture for Super-Resolution Recognition

📋 论文概要

该论文提出了一种基于类皮层架构的多分类器众核处理器，用于超分辨率识别任务，解决了传统单分类器系统在识别准确性和鲁棒性上的不足。通过集成多分类器系统并模拟人类视觉系统的层次结构，实现了646GOPS/W的高能效性能。

💡 主要创新点

• 提出了一种类皮层架构，模拟人类视觉系统的层次化处理流程，提升识别准确率。
• 采用多分类器系统替代单分类器，增强了系统的鲁棒性和识别能力。
• 实现了646GOPS/W的高能效比，适用于自动驾驶、智能监控等实时应用。

🏷 关键词

📄 原文摘要

autonomic vehicle navigation, smart surveillance and unmanned air vehicles (UAVs) [1-3]. Most of the processors adopt a single classifier rather than multiple classifiers even though multi-classifier systems (MCSs) offer more accurate recognition with higher robustness [4]. In addition, MCSs can incorporate the human vision system (HVS) recognition architecture to reduce computational requirements and enhance recognition accuracy. For example, HMAX models the exact hierarchical architecture of the HVS for improved recognition accuracy [5]. Compared with SIFT, known to have the best recognition accuracy based on local features extracted from the object [6], HMAX can recognize an object based on global features by template matching and a maximum-pooling operation

👥 作者与机构

Junyoung Park, Injoon Hong, Gyeonghoon Kim, Youchang Kim,

Kyuho Lee, Seongwook Park, Kyeongryeol Bong, Hoi-Jun Yoo KAIST, Daejeon, Korea Object recognition processors have been reported for the applications of