activity
20182021
most citedAutomatically Harnessing Sparse Acceleration

8 citations · 18 across the 7 of their papers we have counts for

collaborators

15 papers

cs.LG2021

Neural Architecture Search as Program Transformation Exploration

Jack Turner, Elliot J. Crowley, Michael O'Boyle

Improving the performance of deep neural networks (DNNs) is important to both the compiler and neural architecture search (NAS) communities. Compilers apply program transformations…

cs.PL20202 cited

Deep Data Flow Analysis

Chris Cummins, Hugh Leather, Zacharias Fisches +3

Compiler architects increasingly look to machine learning when building heuristics for compiler optimization. The promise of automatic heuristic design, freeing the compiler engine…

cs.PL2020

Modeling Black-Box Components with Probabilistic Synthesis

Bruce Collie, Jackson Woodruff, Michael F. P. O'Boyle

This paper is concerned with synthesizing programs based on black-box oracles: we are interested in the case where there exists an executable implementation of a component or libra…

cs.SE20203 cited

M3: Semantic API Migrations

Bruce Collie, Philip Ginsbach, Jackson Woodruff +2

Library migration is a challenging problem, where most existing approaches rely on prior knowledge. This can be, for example, information derived from changelogs or statistical mod…

cs.LG2020

Optimizing Grouped Convolutions on Edge Devices

Perry Gibson, José Cano, Jack Turner +3

When deploying a deep neural network on constrained hardware, it is possible to replace the network's standard convolutions with grouped convolutions. This allows for substantial m…

cs.PL20203 cited

Retrofitting Symbolic Holes to LLVM IR

Bruce Collie, Michael O'Boyle

Symbolic holes are one of the fundamental building blocks of solver-aided and interactive programming. Unknown values can be soundly integrated into programs, and automated tools s…