Name
Abstract | ||
|---|---|---|
We present R-Stream.TF, a polyhedral optimization tool for neural network computations. R-Stream.TF transforms computations performed in a neural network graph into C programs suited to the polyhedral representation and uses R-Stream, a polyhedral compiler, to parallelize and optimize the computations performed in the graph. R-Stream.TF can exploit the optimizations available with R-Stream to generate a highly optimized version of the computation graph, specifically mapped to the targeted architecture. During our experiments, R-Stream.TF was able to automatically reach performance levels close to the hand-optimized implementations, demonstrating its utility in porting neural network computations to parallel architectures. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-030-17872-7_5 | PROGRAMMING AND PERFORMANCE VISUALIZATION TOOLS |
DocType | Volume | ISSN |
Conference | 11027 | 0302-9743 |
Citations | PageRank | References |
1 | 0.36 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Benoît Pradelle | 1 | 18 | 2.49 |
| Benoît Meister | 2 | 138 | 12.84 |
| Muthu Manikandan Baskaran | 3 | 493 | 33.10 |
| Jonathan Springer | 4 | 15 | 1.69 |
| Richard Lethin | 5 | 118 | 17.17 |