DeepDSL: A Compilation-based Domain-Specific Language for Deep Learning
Authors: Tian Zhao, Xiao Bing Huang, Yu Cao
ICLR 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluated Deep DSL with a number of popular DL networks. Our experiments show that the compiled programs have very competitive runtime performance and memory efficiency compared to the existing libraries. |
| Researcher Affiliation | Academia | Tian Zhao & Xiao Bing Huang Department of Computer Science University of Wisconsin Milwaukee Milwaukee, WI, USA {tzhao,xiaobing}@uwm.edu Yu Cao Department of Computational Neuroscience The University of Massachusetts, Lowell Lowell, MA, USA ycao@cs.uml.edu |
| Pseudocode | No | The paper includes code examples in Scala (Figure 2 and subsequent code blocks) but does not provide structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Deep DSL is available at https://github.com/deepdsl/deepdsl. |
| Open Datasets | Yes | All our tests are trained with Image Net images that have been resized to 224 by 224 |
| Dataset Splits | No | The paper mentions training and testing data but does not provide specific details on validation dataset splits, percentages, or sample counts. |
| Hardware Specification | Yes | The tests are run on a server with a single NVIDIA Tesla K40C GPU equipped with 12 gigabytes of memory. The server runs the Cent OS 7 Linux distribution. |
| Software Dependencies | Yes | Deep DSL uses the JCuda 0.8.0RC binding that runs against CUDA 8.0.279. |
| Experiment Setup | Yes | Figure 2 shows the complete implementation for compiling a program to train and test Lenet... val solver = Train('lenet', 1000, 10, 0.01f, 0.9f, 0.0005f, 0) // output file, train and test iteration, learn rate, momentum, decay, gradient cropping (0 means none) |