Learning Notes - Edge Devices - Bench marking

Paper #1 - A Survey on Edge Benchmarking

Edge benchmarking parameters

• I/O throughput
• Data staleness
• End-to-end communication or computation latency

Devices

• Intel Movidius Myriad X VPU
• NVIDIA 128-core Maxwell and 256-core Pascal architecture-based GPU
• Google Edge TPU

Paper #2 - MLPERF TRAINING BENCHMARK
Tasks Considered

• Image classification
• Object detection (lightweight)
• Instance segmentation and object detection (heavyweight)
• Recommendation
• Reinforcement learning 

Modifiable Hyperparameters

• Batch size, Learning-rate schedule parameters
• Optimizer: Adam or Lazy Adam, Learning rate
• Maximum samples per training patch

MLPerf Training v0.6 Results
Paper #3 - Early Experience in Benchmarking Edge AI Processors with Object Detection Workloads

Paper #4 - pCAMP: Performance Comparison of Machine Learning Packages on the Edges

More Reads

• A Hybrid Approach to Privacy-Preserving Federated Learning
• EdgeCNN: Convolutional Neural Network Classification Model with small inputs for Edge Computing
• The Big Benchmarking Roundup
• How to set up a Postgres database on a Raspberry Pi
• A Step by Step guide to installing PyTorch in Raspberry Pi
• Federated Learning On Raspberry Pi
• FEDERATED LEARNING OF A RECURRENT NEURAL NETWORK ON RASPBERRY PIS
• How to set up a Raspberry Pi with Ubuntu from almost any device, completely headless, with just WiFi
• Object Detection on NVIDIA Jetson TX2
• Benchmarking Edge Computing

Keep Thinking!!!