🌟 Exciting Developments in Edge AI Optimization at NeuroFlex!🌟

[kasinadhsarma]

We’ve been diligently testing various optimization algorithms to enhance our Edge AI models, and I’m thrilled to share some preliminary benchmarking results!

🔍 Benchmarking Results Overview:

1. Adam Optimizer:

   • Accuracy: 0.17
   • Training Time: 4.918 seconds
   • Inference Time: 0.137 seconds

2. RMSprop Optimizer:

   • Accuracy: 0.19
   • Training Time: 5.045 seconds
   • Inference Time: 0.136 seconds

3. Mini-batch Gradient Descent:

   • Accuracy: 0.26
   • Training Time: 4.480 seconds
   • Inference Time: 0.138 seconds

4. Hybrid Approach:

   • Accuracy: 0.26
   • Training Time: 5.267 seconds
   • Inference Time: 0.136 seconds

✨ Key Insights:

• Both the Mini-batch Gradient Descent and Hybrid Approach achieved the highest accuracy of 0.26, with Mini-batch Gradient Descent leading in training time.
• The Adam optimizer, while slightly less accurate, still demonstrates competitive performance metrics.

As we continue to refine our models, these insights will guide our efforts to enhance performance and efficiency in Edge AI applications.

Stay tuned for more updates as we dive deeper into optimization techniques and explore new possibilities in Edge AI!

#EdgeAI #NeuroFlex #AI #MachineLearning #Optimization
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