The Advanced Computing Lab

Created September 2024

The Advanced Computing Lab (ACL) in the AI Division at Carnegie Mellon University's Software Engineering Institute (SEI) has extensive expertise in optimizing software performance on diverse hardware architectures, and hardware and system design for software-based systems. The ACL applies this expertise to systems utilizing machine learning in the interests of national security.

Work With Us

What We Do

ACL capabilities and areas of expertise include the following:

AI/ML accelerator benchmarking
Algorithmic performance optimization
Compiler design and assessment
Data-intensive computing
Edge computing and SWaP-C constraints
Field Programmable Gate Array (FPGA) design and system integration
Heterogeneous computing
High Performance Computing
Homomorphic encryption
Machine learning enhanced physics simulation
Neuromorphic computing
Physics-inspired neural networks
Quantum computing and algorithms
Software Defined Radio
Sparse linear algebra and graphs

Sampling of Current and Recent Projects

Recent ACL projects of note include the following:

Hardware Software Co-design: The Co-design for Edge AI internal research and development funded project is targeting development of System on Chips (SoCs) targeted for specific applications. The technology focus is on high level synthesis for FPGAs. Learn more:

Software-Hardware Codesign for Machine Learning Workloads (workshop)
Co-Design for Edge AI: Application-Specific System on Chip (article)
Research Review 2023: Co-design for Edge AI (video)

Portable High-Performance Inference on the Tactical Edge (PHITE): PHITE applies performance engineering processes to the analysis of existing open-source ML frameworks for embedded systems, to inform the development and optimization of a portable software library that can achieve significantly higher performance across a range of targeted embedded devices. Learn more:

PHITE: Portable High-Performance Inference at the Tactical Edge (presentation)
Portable High-Performance Inference on the Tactical Edge (PHITE) (article)
PHITE: Improved Situational Awareness through AI at the Edge (fact sheet)
Preview of PHITE: Portable High-Performance Inference at the Tactical Edge (video)

Spiral AI/ML: Enabling platform developers to realize high-performance AI/ML applications on leading-edge hardware architectures faster and cheaper. Learn more:

Spiral/AIML: Resource-Constrained Co-Optimization for High-Performance, Data-Intensive Computing (article)
Spiral AI/ML Collection (collection)
Spiral/AIML: Frontiers of Graph Processing in Linear Algebra (poster)

Test and Evaluation for DARPA ERI programs: The ACL acts as an Expert First User for the government team on a multitude of DARPA ERI programs. Learn more: