.png){kind=icon}
返回
Enabling Resource-Efficient Edge Intelligence with Compressive Sensing-Based Deep Learning
.png){kind=icon}
PRE
AI
DOI：10.1145/3528416.3530230.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Billions of sensor-enabled computing devices open tremendous opportunities for AI-powered context-aware services. Yet, democratizing AI so that heterogeneous devices can enjoy deep learning (DL) revolution requires significant reduction in computing and energy burden posed by DL models. Inspired by the concept of compressive sensing (CS), and guided by the observation that reduced sampling rates often suffice for successful classification, we devise an adaptive CS-DL pipeline. Our approach dynamically adjusts the sensing rate according to input properties and performs classification through an input-flexible DL model, demonstrating classification accuracy rates on par with uncompressed models while using up to 46% less battery energy.
Keyword:
neural networks
compressive sensing
human activity recognition
期刊
机构
引用论文
分享收藏DR2-Net: Deep Residual Reconstruction Network for image compressive sensingDR2-Net: 深度残差重构网络图像压缩感知.png)
NEUROCOMPUTING
OAAI
NEUROCOMPUTINGIF6.5
OAAI分享收藏Multiple Autism-Linked Genes Mediate Synapse Elimination via Proteasomal Degradation of a Synaptic Scaffold PSD-95多个自闭症相关基因通过突触支架PSD-95的蛋白酶体降解介导突触消除CellOAAI
CellIF0
OAAI分享收藏分享收藏
分享收藏
查看更多内容