2 citations · 3 across the 3 of their papers we have counts for
5 papers
Pattern Matching in AI Compilers and its Formalization (Extended Version)
Joseph W. Cutler, Alex Collins, Bin Fan +2
PyPM is a Python-based domain specific language (DSL) for building rewrite-based optimization passes on machine learning computation graphs. Users define individual optimizations b…
TinyIREE: An ML Execution Environment for Embedded Systems from Compilation to Deployment
Hsin-I Cindy Liu, Marius Brehler, Mahesh Ravishankar +3
Machine learning model deployment for training and execution has been an important topic for industry and academic research in the last decade. Much of the attention has been focus…
Composable and Modular Code Generation in MLIR: A Structured and Retargetable Approach to Tensor Compiler Construction
Nicolas Vasilache, Oleksandr Zinenko, Aart J. C. Bik +9
Despite significant investment in software infrastructure, machine learning systems, runtimes and compilers do not compose properly. We propose a new design aiming at providing unp…
Automatic acceleration of Numpy applications on GPUs and multicore CPUs
Mahesh Ravishankar, Vinod Grover
Frameworks like Numpy are a popular choice for application developers from varied fields such as image processing to bio-informatics to machine learning. Numpy is often used to dev…
Beyond Reuse Distance Analysis: Dynamic Analysis for Characterization of Data Locality Potential
Naznin Fauzia, Venmugil Elango, Mahesh Ravishankar +5
Emerging computer architectures will feature drastically decreased flops/byte (ratio of peak processing rate to memory bandwidth) as highlighted by recent studies on Exascale archi…