Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
Authors: Albert Gural, Boris Murmann
ICML 2019 | Venue PDF | LLM Run Details | Input Tokens: 21,103 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,170 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate the memory-optimal CNN technique with an Arduino implementation of the 10-class MNIST classification task, fitting the network specification, weights, and activations entirely within 2KB SRAM and achieving a state-of-the-art classification accuracy for small-scale embedded systems of 99.15%. |
| Researcher Affiliation | Academia | Albert Gural 1 Boris Murmann 1 1Department of Electrical Engineering, Stanford University, Stanford, USA. |
| Pseudocode | No | The paper describes algorithmic strategies but does not include any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and supplemental material are available here. |
| Open Datasets | Yes | a test accuracy of 99.15% is achievable for the original MNIST-10 dataset from Le Cun et al. (1998). |
| Dataset Splits | No | A network architecture search is performed to find the best quantized network by validation performance. ... for all 10,000 test images to ensure a 100% match. (No explicit train/validation split percentages or counts are provided). |
| Hardware Specification | Yes | A single Arduino based on the ATmega328P chip is employed for the classification task. |
| Software Dependencies | No | The network is trained in Keras/Tensor Flow with Adam (Abadi et al., 2015; Chollet et al., 2015; Kingma & Ba, 2014) (No specific version numbers are provided for the software components). |
| Experiment Setup | Yes | The network is trained in Keras/Tensor Flow with Adam (Abadi et al., 2015; Chollet et al., 2015; Kingma & Ba, 2014) for 50 epochs in floating point and 200 epochs with 4-bit quantized training using the straight-through estimator and ALT training (Courbariaux et al., 2016; Jain et al., 2019). |