Red Hat Collaboratory Systems Seminar: Collaboratory Student Research Presentation
Title: Exploring Dynamic Off-Loading of Execution to Neural Networks
Speaker: CJ Para and Patrick Browne
Abstract: 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. Specifically, we explore the ability of ANNs to learn and predict a system’s future behavior by interfacing them with a binary “state vector” representation of the computer’s registers and memory. Experimentally, we utilize a simple 6502 simulator and chess program, along with ANNs implemented on GPUs using PyTorch, to conduct a concrete, data-driven study. Our methodology involves extensive data collection and training of neural networks, aiming to quantify the extent to which low-level operation can reveal meaningful program behavior. The methods and infrastructure developed in this work are intended to be a foundation for studying if, by exposing a computer program’s execution to ML mechanisms, opportunities for accelerations can be automatically discovered.
Boston University Center for Computing & Data Sciences
665 Commonwealth Ave., Room 1101 (11th floor)
Boston, MA 02215
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About the Collaboratory: A partnership between Red Hat and Boston University, the Red Hat Collaboratory connects BU faculty and students with industry practitioners working in open-source software communities. The Collaboratory aims to advance research focused on emerging technologies in a number of areas including operating systems, cloud computing services, machine learning and automation, and big data platforms.