Efficient embedded learning for IoT devices

By Swagath Venkataramani, Kaushik Roy, Anand Raghunathan. 2016

36 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Hardware and Architecture

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Electrical and Electronic Engineering

1. Ala Al-Fuqaha
2. M. Mohammadi
3. Shree Krishna Sharma
4. Mohsen Guizani
5. Sameh Sorour
6. Xianbin Wang

In Hardware and Architecture

1. Ala Al-Fuqaha
2. Mohsen Guizani

Citations in the corpus

• Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “In [153], several approaches for improving the intelligence of IoT devices are identified including designing accelerators for DNNs, and using Post-CMOS technologies such as spintronics that employs electron spinning mechanism [154].”
• Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018, background): “In this regard, some of the potential future solutions to improve computational efficiency of ML algorithms include deep-learning accelerators, approximate computing and post-CMOS device technologies [179].”