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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Espresso: Efficient Forward Propagation for Binary Deep Neural Networks
Authors: Fabrizio Pedersoli, George Tzanetakis, Andrea Tagliasacchi
ICLR 2018 | Venue PDF | 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 EMAIL George Tzanetakis University of Victoria EMAIL Andrea Tagliasacchi University of Victoria EMAIL |
| 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 con๏ฌgure 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. |