Reliable Edge Intelligence in Unreliable Environment
Minah Leea, Xueyuan Sheb, Biswadeep Chakrabortyc, Saurabh Dashd, Burhan Mudassare and Saibal Mukhopadhyayf
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
aminah.lee@gatech.edu
bxshe@gatech.edu
cbiswadeep@gatech.edu
dsaurabhdash@gatech.edu
eburhan.mudassar@gatech.edu
fsmukhopadhyay6@gatech.edu
ABSTRACT
A key challenge for deployment of artificial intelligence (AI) in real-time safety-critical systems at the edge is to ensure reliable performance even in unreliable environments. This paper will present a broad perspective on how to design AI platforms to achieve this unique goal. First, we will present examples of AI architecture and algorithm that can assist in improving robustness against input perturbations. Next, we will discuss examples of how to make AI platforms robust against hardware induced noise and variation. Finally, we will discuss the concept of using lightweight networks as reliability estimators to generate early warning of potential task failures.
Keywords: Autonomous System, Edge Intelligence, Machine Learning, Reliability.
