Data redundancy is ubiquitous in the inputs and intermediate results of Deep Neural
Networks (DNN). It offers many significant opportunities for improving DNN performance and …

Over the last decade, machine learning (ML) and deep learning (DL) algorithms have
significantly evolved and been employed in diverse applications, such as computer vision …

Deep neural networks (DNNs) have shown remarkable success in various machine-learning
(ML) tasks useful for many safety-critical, real-time embedded systems. The foremost design …

The paper discusses algorithmic priority inversion in mission-critical machine inference
pipelines used in modern neural-network-based cyber-physical applications, and develops …

We propose Zygarde--which is an energy--and accuracy-aware soft real-time task
scheduling framework for batteryless systems that flexibly execute deep learning tasks1 that …

For realizing safe autonomous driving, the end-to-end delays of real-time object detection
systems should be thoroughly analyzed and minimized. However, despite recent …

While high accuracy is of paramount importance for deep learning (DL) inference, serving
inference requests on time is equally critical but has not been carefully studied especially …

Online inference is becoming a key service product for many businesses, deployed in cloud
platforms to meet customer demands. Despite their revenue-generation capability, these …

This paper explores criticality-based real-time scheduling of neural-network-based machine
inference pipelines in cyber-physical systems (CPS) to mitigate the effect of algorithmic …

This paper presents a self-cueing real-time frame-work for attention prioritization in AI-
enabled visual perception systems that minimizes a notion of state uncertainty. By attention …