Datasets:

fastmachinelearning
/

wa-hls4ml

@@ -189,11 +189,11 @@ The exemplar models utilized in this study include several key architectures, ea
 The data was generated via randomly generated neural networks and specifically selected exemplar models, converted into HLS Code via hls4ml, with the resulting latency values collected after performing C-Synthesis through Vivado/Vitis HLS on the resulting HLS Code, and resource values collected after performing logic synthesis through Vivado/Vitis on the resulting HDL Code.
 ### Who are the source data producers?
-[Benjamin Hawks](orcid.org/0000-0001-5700-0288), Fermi National Accelerator Laboratory, USA
-[Hamza Ezzaoui Rahali](orcid.org/0000-0002-0352-725X), University of Sherbrooke, Canada
-[Mohammad Mehdi Rahimifar](orcid.org/0000-0002-6582-8322), University of Sherbrooke, Canada
 ### Personal and Sensitive Information
@@ -204,10 +204,9 @@ This data contains no personally identifiable or sensitive information except fo
 In it's inital form, a majority of this dataset is comprised of very small (2-3 layer) dense neural networks without activations. This should be considered when training a model on it, and appropriate measures should be taken to weight the data at training time.
 We intend to continuously update this dataset, addressing this imbalance over time as more data is generated.
 ### Recommendations
-Appropriate measures should be taken to weight the data to account for the imbalance at training time.
 ## Citation [optional]
@@ -222,11 +221,11 @@ Paper currently in review.
 [More Information Needed]
 ## Dataset Card Authors
-[Benjamin Hawks](orcid.org/0000-0001-5700-0288), Fermi National Accelerator Laboratory, USA
-[Hamza Ezzaoui Rahali](orcid.org/0000-0002-0352-725X), University of Sherbrooke, Canada
-[Mohammad Mehdi Rahimifar](orcid.org/0000-0002-6582-8322), University of Sherbrooke, Canada
 ## Dataset Card Contact

 The data was generated via randomly generated neural networks and specifically selected exemplar models, converted into HLS Code via hls4ml, with the resulting latency values collected after performing C-Synthesis through Vivado/Vitis HLS on the resulting HLS Code, and resource values collected after performing logic synthesis through Vivado/Vitis on the resulting HDL Code.
 ### Who are the source data producers?
+[Benjamin Hawks](https://orcid.org/0000-0001-5700-0288), Fermi National Accelerator Laboratory, USA
+[Hamza Ezzaoui Rahali](https://orcid.org/0000-0002-0352-725X), University of Sherbrooke, Canada
+[Mohammad Mehdi Rahimifar](https://orcid.org/0000-0002-6582-8322), University of Sherbrooke, Canada
 ### Personal and Sensitive Information
 In it's inital form, a majority of this dataset is comprised of very small (2-3 layer) dense neural networks without activations. This should be considered when training a model on it, and appropriate measures should be taken to weight the data at training time.
 We intend to continuously update this dataset, addressing this imbalance over time as more data is generated.
 ### Recommendations
+Appropriate measures should be taken to weight the data to account for the dataset imbalance at training time.
 ## Citation [optional]
 [More Information Needed]
 ## Dataset Card Authors
+[Benjamin Hawks](https://orcid.org/0000-0001-5700-0288), Fermi National Accelerator Laboratory, USA
+[Hamza Ezzaoui Rahali](https://orcid.org/0000-0002-0352-725X), University of Sherbrooke, Canada
+[Mohammad Mehdi Rahimifar](https://orcid.org/0000-0002-6582-8322), University of Sherbrooke, Canada
 ## Dataset Card Contact