Mr. Sean Harding Profile

Sean Harding

at DEVCOM Army Research Lab

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 7 June 2024 Presentation + Paper

Stochastic optimization of inference execution on tactical edge networks

Mikal Willeke, Sean Harding, Kevin Chan, Nathaniel Bastian

Proceedings Volume 13051, 130510X (2024) https://doi.org/10.1117/12.3013136

KEYWORDS: Stochastic processes, Mathematical optimization, Intelligence systems, Statistical modeling, Algorithm development, Systems modeling, Statistical analysis

Read Abstract +

In tactical edge networks, the volatility of computational and communication resources complicates the consistent processing of data. Previous work has developed a system that allows execution of inference task applications throughout a network using a variety of adaptations to machine learning models that offer accuracy and latency trade-offs as conditions change in order adaptively perform deterministic resource allocation at the tactical edge. This paper expands on previous work that proposed a system that allows execution of inference task applications throughout a network and then developed a resource allocation algorithm that optimally places intelligence, surveillance, and reconnaissance related machine learning tasks throughout the network. We propose utilizing stochastic optimization to analyze the computational time and performance for inference tasks. Instead of adhering to deterministic averages, we use sample average approximation as a solution technique to optimize and analyze the inherent uncertainties of tactical edge environments, optimizing over the distribution of inference data rather than average-case scenarios. This paper verifies Jensen’s inequality gap within deterministic optimization and proposes an improved resource allocation algorithm that optimally places tasks throughout a network under uncertainty. We present initial results on a military relevant tactical edge network scenario.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years