Welcome to the MGHPCC Virtual Booth

Northeastern University

Towards Best Possible Deep Learning Acceleration on the Edge – A Compression-Compilation Co-Design Framework

Tuesday, November 17 2:00 pm EST
Passcode: 759941
Assuming hardware is the major constraint for enabling real-time mobile intelligence, the industry has mainly dedicated their efforts to develop specialized hardware accelerators for machine learning and inference. In this presentation, we show that by drawing on a recent real-time AI optimization framework CoCoPIE that achieves effective compression- compiler co-design, it is possible to enable real-time artificial intelligence on mainstream end devices without special hardware.
Northeastern University
Electrical and Computer Engineering
Ms. Yifan Gong
PhD student
Chat with us @
Attend Consortium Talks @


Featured projects

Air Force Arcade
Airborne Optical Systems Test Bed (AOSTB)
Black Hole Initiative
Center for Scientific Computing and Visualization Research (CSCVR)
Data Centric Low Emission Mobility
Fast Accurate NURBS Optimization (FANO)
Fusarium Pathogenomics: Understanding Fungal Pathogenicity through Genomics
GLEAM: Global Epidemic and Mobility project
Lichtman Lab - Center for Brain Science
Lincoln Laboratory Supercomputing Center (LLSC)
LLSC Articles and Publications
MIT Initiative on the Digital Economy
MIT Laboratory for Financial Engineering
Northeast Cyberteam
Northeastern University and Williams College SC20 Student Cluster Competition Team
Quantum Architectures at Goodwill Computing Lab
Simulating Large Biomolecular Assemblies
The Center for Information and Systems Engineering (CISE)
The Mass Open Cloud and Open Cloud Initiative
Video and Imagery Dataset to Drive Public Safety
Visibility Estimation through Image Analytics (VEIA)
linkedin facebook pinterest youtube rss twitter instagram facebook-blank rss-blank linkedin-blank pinterest youtube twitter instagram