.png){kind=icon}
返回
Optimized Hierarchical Cascaded Processing
.png){kind=icon}
PRE
AI
DOI：10.1109/JETCAS.2018.2839347.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Recently, there has been an increasing demand for advanced classification capabilities embedded on wearable battery constrained devices, such as smartphones or watches. Achieving such functionality with a tight power and energy budget has proven a real challenge, specifically for large-scale neural network-based applications. Previously, cascaded systems have been proposed to minimize energy consumption for such applications, either through using a single wake-up stage, or by using a linear- or tree based cascade of consecutive classifiers that allow early termination. In this paper, we expand upon these concepts by generalizing cascades to hierarchical cascaded processing, where a hierarchy of increasingly complex classifiers, each designed and trained for a specific subtask is used. This hierarchical approach significantly outperforms the wake-up based approach by up to 2 orders of magnitude in energy consumption at iso-accuracy, specifically in systems with sparse input data such as speech recognition and visual object detection. This paper presents a general design framework for such systems and illustrates how to optimize them toward minimum energy consumption. The text further proposes a roofline model for cascaded systems, derives system level trade-offs and proves the approaches validity through a visual classification case-study.
Keyword:
Hierarchical cascaded processing
hierarchical systems
cascaded systems
deep learning
object recognition
ubiquitous computing
hierarchical cascaded processing
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.8
论文数:
1.4K
被引数:
2.8K
机构
引用论文
.png){kind=icon}
没有更多内容
没有更多内容