Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Training DNNs with Hybrid Block Floating Point
Authors: Mario Drumond, Tao LIN, Martin Jaggi, Babak Falsafi
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment with a set of popular image classification tasks with the CIFAR-100, SVHN, and Image Net datasets. |
| Researcher Affiliation | Academia | Mario Drumond Ecocloud EPFL EMAIL Tao Lin Ecocloud EPFL EMAIL Martin Jaggi Ecocloud EPFL EMAIL Babak Falsafi Ecocloud EPFL EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | We experiment with a set of popular image classification tasks with the CIFAR-100 [19], SVHN [20], and Image Net [21] datasets. We also evaluate language modeling tasks with Penn Tree Bank(PTB) dataset [24]. |
| Dataset Splits | No | For the image classification experiments, we report training loss and validation top-1 error. For the language modeling models, we report training loss and validation perplexity. While these datasets have standard splits, the paper doesn't explicitly state what splits were used for reproducibility. |
| Hardware Specification | Yes | We synthesize the accelerator on a Stratix V 5SGSD5 FPGA at a clock rate of 200MHz. |
| Software Dependencies | No | The paper mentions modifying Py Torch's layers but does not specify the version number of Py Torch or any other software dependencies with their versions. |
| Experiment Setup | No | The paper states, 'We trained all models using the same hyperparameters reported in their respective original papers,' which means the specific hyperparameter values are not directly provided within this paper. |