Espresso: Efficient Forward Propagation for Binary Deep Neural Networks
Authors: Fabrizio Pedersoli, George Tzanetakis, Andrea Tagliasacchi
ICLR 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally show that Espresso is significantly faster than existing implementations of optimized binary neural networks ( 2 orders of magnitude). |
| Researcher Affiliation | Academia | Fabrizio Pedersoli University of Victoria fpeder@uvic.ca George Tzanetakis University of Victoria gtzan@uvic.ca Andrea Tagliasacchi University of Victoria ataiya@uvic.ca |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Espresso is released under the Apache 2.0 license and is available at http://github.com/fpeder/espresso. |
| Open Datasets | Yes | The MNIST dataset (Le Cun et al., 1998) consists of 60K instances for training and, 10K instances for testing. The CIFAR-10 dataset (Krizhevsky et al., 2009), consists of 50K training instances and 10K testing instances of 32 32 3 color images. |
| Dataset Splits | No | The paper specifies training and testing instances for datasets but does not explicitly mention a validation set or split for reproducibility. |
| Hardware Specification | Yes | The execution times, averaged over 100 experiments, are obtained on a machine equipped with an NVIDIA Ge Force GTX 960 with 2GB of RAM, and a Intel R dual-Xeon R X5660 @ 2.80 GHz. |
| Software Dependencies | No | The paper mentions using the 'Open BLAS library (Xianyi et al.)' but does not specify a version number for it or any other software dependency. |
| Experiment Setup | Yes | In CPU mode, we configure the Open BLAS library for matrix multiplication to use all the 24 available cores. Since our interest is to asses the real-time performance of binary optimized DNNs, in those experiment we use a batch-size of one, and measure the averaged forward time for each image of the testing-sets for each dataset. |