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  • End of Month 2: Completion of asset transfers and basic project setup.
  • End of Month 3: Initial project integration into LF Edge and establishment of communication channels.
  • End of Month 5: Formation and activation of the Technical Steering Committee.
  • End of Month 6: Establishment of a stable governance framework and active community engagement.

Benchmarking Methodology

  1. Introduction: A brief overview of the importance of benchmarking in the context of AI at the edge. This section will set the stage by explaining why benchmarking is crucial for assessing the efficiency, performance, and scalability of AI edge technologies.

  2. Benchmarking Criteria:

    • Performance Metrics: Description of the key performance indicators (KPIs) used in benchmarking, such as latency, throughput, power consumption, and accuracy.
    • Hardware and Software Considerations: Outline of the hardware platforms (e.g., CPUs, GPUs, Edge TPUs) and software frameworks (e.g., TensorFlow, PyTorch) included in the benchmarks.
    • Test Scenarios and Use Cases: Elaboration on various scenarios and use cases for which the benchmarks are applicable, providing context and relevance.
  3. Methodology:

    • Test Environment Setup: Detailed description of the test environment, including hardware specifications, software versions, and network configurations.
    • Data Collection and Analysis: Explanation of the process for collecting and analyzing data during benchmark tests.
    • Reproducibility and Transparency: Emphasis on the importance of reproducibility in benchmarking, including guidelines for documenting and sharing test procedures and results.
  4. Results and Reporting:

    • Presentation of Results: Guidelines on how to effectively present benchmarking results, including visualization techniques and comparative analysis.
    • Interpreting Results: Tips on interpreting results, understanding limitations, and drawing meaningful conclusions.
  5. Continuous Improvement:

    • Feedback Loop: Encouraging community feedback and contributions to refine and enhance the benchmarking methodology.
    • Updates and Versioning: Details on how the benchmarking methodology will be updated over time, including version control and change logs.