Publications

Written by Jim Blakley | December 4, 2019 (Note: This Blog is a republication of my final blog while at Intel.) Almost three years ago, Carnegie Mellon University Prof. Dave Andersen and I announced the Intel Science and Technology Center for Visual Cloud Systems (ISTC-VCS) at the 2016 NAB Show. Along

Latency-sensitive edge-native applications may be the key to commercial success of edge infrastructure. However, success in the form of widespread deployment of such applications poses its own challenges.

We introduce the concept of "edge-native applications" that fully exploit the potential of edge computing and have a deeply symbiotic relationship with it.

This paper investigates the use of drones for live inspection in the  construction industry. The key technical challenge is the real-time  registration of the drone video feed to the architectural plan. We  present and evaluate three different approaches for registration and  propose an edge-based prototype using visual features. Our evaluations

Real-time traffic monitoring has had widespread success via  crowd-sourced GPS data. While drivers benefit from this low-level,  low-latency road information, any high-level traffic data such as road  closures and accidents currently have very high latency as such systems  rely solely on human reporting. Increasing the detail and decreasing the  latency

Many emerging mobile applications, including augmented reality (AR) and  wearable cognitive assistance (WCA), aim to provide seamless user  interaction. However, the complexity of benchmarking these  human-in-the-loop applications limits reproducibility and makes  performance evaluation difficult. In this paper, we present EdgeDroid, a  benchmarking suite designed to reproducibly evaluate these  applications. Our

This paper shows how today's complex computing landscape can be  understood in simple terms through a 4-tier model. Each tier represents a  distinct and stable set of design constraints that dominate attention  at that tier. There are typically many alternative implementations of  hardware and software at each tier, but all

Edge computing processes data on infrastructure that is located close to the point of data creation. Mahadev Satyanarayanan recounts how recognition of the potential limitations of centralized, cloud-based processing led to this new approach to computing. Satyanarayanan, M. Nature Electronics, 2(1), January 2019

Real-time video analytics on small autonomous drones poses several  difficult challenges at the intersection of wireless bandwidth,  processing capacity, energy consumption, result accuracy, and timeliness  of results. In response to these challenges, we describe four  strategies to build an adaptive computer vision pipeline for search  tasks in domains such as

The generation of high-quality training data has become the key  bottleneck in the use of deep learning across many domains. We describe  Eureka, an interactive system that leverages edge computing and early  discard to greatly improve the productivity of experts in the  construction of a labeled data set. Our experimental

Recently, edge computing has emerged as a promising computing paradigm  to meet stringent quality-of-service requirements of an increasing  number of latency-sensitive applications. The core principle of edge  computing is to bring the capability of cloud computing in close  proximity to mobile devices, sensors, actuators, connected things and  end users, thereby