WRPN: Wide Reduced-Precision Networks
Authors: Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook, Debbie Marr
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report results and show that WRPN scheme is better than previously reported accuracies on ILSVRC-12 dataset while being computationally less expensive compared to previously reported reduced-precision networks. |
| Researcher Affiliation | Industry | Asit Mishra, Eriko Nurvitadhi, Jeffrey J Cook & Debbie Marr Accelerator Architecture Lab Intel Labs {asit.k.mishra,eriko.nurvitadhi,jeffrey.j.cook,debbie.marr}@intel.com |
| Pseudocode | No | The paper describes its quantization scheme using mathematical formulas (Equation 2) but does not provide structured pseudocode or an algorithm block. |
| Open Source Code | No | The paper mentions using 'tensorpack' and provides its GitHub link (https://github.com/ppwwyyxx/tensorpack), but this is a third-party framework used by the authors, not their own source code for the WRPN methodology. |
| Open Datasets | Yes | We report results on Alex Net (Krizhevsky et al., 2012), batch-normalized Inception (Ioffe & Szegedy, 2015), and Res Net-34 (He et al., 2015) on ILSVRC12 (Russakovsky et al., 2015) dataset. (Russakovsky et al., 2015) refers to 'Image Net Large Scale Visual Recognition Challenge.' |
| Dataset Splits | Yes | We use Tensor Flow (Abadi et al., 2015) and tensorpack for all our evaluations and use ILSVRC-12 train and val dataset for analysis. |
| Hardware Specification | Yes | For GPU, we evaluate WRPN on Nvidia Titan X Pascal and for FPGA we use Intel Arria-10. Our RTL design targets Arria-10 1150 FPGA. For our ASIC study, we synthesize the PE design using Intel 14 nm process technology to obtain area and energy estimates. |
| Software Dependencies | No | The paper states, 'We use Tensor Flow (Abadi et al., 2015) and tensorpack for all our evaluations', but it does not specify version numbers for these software dependencies. |
| Experiment Setup | No | The paper states, 'all the runs have same hyper-parameters and training is carried out for the same number of epochs as baseline network' and 'We used the same hyper-parameters and learning rate schedule as the baseline network.' However, it does not explicitly list the specific values of these hyperparameters or the learning rate schedule. |