Objective

FAiNDER's Objective

To assist researchers in navigating extensive AI research data efficiently. Our platform offers a comprehensive, filterable table and interactive charts, making it easier to digest and analyze vast amounts of information.

FAiNDER Objective
Methodology Design

Methodology

Our Process

We believe it is important to detail our methodology to ensure the validity and reliability of the data we present.

Source

We gather information from major conferences (IPDPS, MICRO, ISCA, HPCA, PACT) and leading companies to cover the latest AI advancements.

Data Processing

We synthesize data into concise summaries, focusing on hardware requirements and AI model performance, providing relevant and actionable insights.

Interactive Data Presentation

We validate our data by cross-referencing multiple sources to ensure accuracy and reliability.

Include Us in Your Research

MLA: “FAiNDER.” FAiNDER, Barcelona Supercomputing Center, fainder.eu/. Accessed 27 September 2024.

APA: Barcelona Supercomputing Center. (n.d.). FAiNDER. FAiNDER. https://fainder.eu

Chicago: “FAiNDER.” FAiNDER. Accessed September 27, 2024. https://fainder.eu/.

Harvard: FAiNDER (2024) FAiNDER. Available at: https://fainder.eu/ (Accessed: 27 September 2024).

Vancouver: FAiNDER [Internet]. Barcelona Supercomputing Center; [cited 2024 September 27]. Available from: https://fainder.eu/

BibTeX: @misc{FAiNDER, url={https://fainder.eu/}, journal={FAiNDER}, publisher={Barcelona Supercomputing Center}}

Team

The FAiNDER Team

Our research team specializes in computer architecture and is dedicated to advance the future of AI technology.

Javier Beiro

Javier Beiro

RNNs, DLRS & Transformers

Mariana Carmin

Mariana Carmin

DNNs, CNNs & GNNs

Victor Xirau

Victor Xirau

Developer & Tech Support

Part of the Barcelona Supercomputing Center Memory Team

Experts in hardware architecture and high-performance computing (HPC) is dedicated to advancing next-generation memory systems. Through our research, we aim to enhance memory system design to meet the rigorous demands of future HPC applications.

Frequently Asked Questions

We've compiled a list of commonly asked questions to provide you with quick and informative answers.

  • How can I search for information?

    You can use the filters on the Explorer page to search for specific AI models and data.

  • How can I check which models are trained using CPUs?

    Select the "CPU" option in the hardware filter on the Explorer page.

  • How can I get updates about new information?

    You can subscribe to our newsletter to receive email updates.

  • I have some questions to discuss.

    Feel free to reach out to our support team through the contact page.

  • How can I check which models use FP32 in training?

    Look for the data type filter and select "FP32" under the training phase.

  • How can I check models with sparse data?

    You can find this information by filtering models with sparsity features in the Explorer.

Still Have a Question?

If your question wasn't answered in the FAQs, please feel free to contact us directly.