Machine learning enhanced high-level synthesis research wins best paper at Quality Electronic Design conference
“AutoAnnotate: Reinforcement-learning-based code annotation for high-level synthesis,” a paper resulting from a project at the Red Hat Collaboratory at Boston University, was selected as one of four Best Papers at the 25th International Symposium on Quality Electronic...How research is driving AI at the edge, smart cities, and RISC-V at the 2024 Red Hat Summit
Planning to attend the 2024 Red Hat Summit, May 6-9, in Denver, CO? Topics related to Red Hat Research, the MOC Alliance, New England Research Cloud, and the Red Hat Collaboratory at Boston University will be featured in several presentations on popular subjects like...Co-design research lab accelerates innovation in non-traditional and specialized hardware
By Ahmed Sanaullah In 2023, Red Hat Research announced the launch of the Co-Design (CoDes) research lab during the Massachusetts Open Cloud (MOC) Alliance Workshop. Our goal was to build an ecosystem that could deliver on the immense value proposition of...Red Hat Collaboratory Systems Seminar: Collaboratory Student Research Presentation
Our research focuses on determining whether we can use Artificial NeuralNetworks (ANNs) to predict and improve computer program execution based on a computer’s low-level machine state. Building on Professor Appavoo’s work on Automatically Scalable Computation (ASC) (ASPLOS 2014) and subsequent developments like DANA (PACT 2015) and SEUSS (EUROSys 2020), we explore the use of modern ML approaches to represent and learn emergent structure from a low-level binary representation of a computer systems operation.