Skip to content

Benchmark Tools for tiny AI/ML Models

A practical guide to benchmark tiny machine learning models using SOTA tools. Tiny AI models are optimized for edge devices, mobile platforms, or low-latency applications.

Why Benchmark tiny Models?

Efficient benchmarking helps assess their speed, memory usage, and accuracy in real-world scenarios.

Benefits: - Ensure low-latency performance for real-time applications.
- Optimize for deployment on resource-constrained devices.
- Compare different tools and hardware to find the best fit for your use case.

Benchmarking with OpenVINO

OpenVINO is a highly efficient tool for benchmarking vision models, especially for Intel hardware. Follow these steps to benchmark your model:

Step 1: Install OpenVINO Toolkit

pip install openvino-dev

Step 2: Convert Your Model to IR Format (Optional)

If your model is not already in OpenVINO's Intermediate Representation (IR) format, convert it: 

mo --input_model path/to/your/model.onnx --output_dir path/to/output

Step 3: Run the Benchmark Tool

Use OpenVINO's Benchmark Tool for performance evaluation: 

benchmark_app -m path/to/your/model.xml -d CPU -niter 100

Step 4: Analyze the Output

  • Throughput: Number of inferences per second.
  • Latency: Average time taken for a single inference.

Tips for Optimization

  • Add -d GPU for GPU acceleration.
  • Use quantized models for better performance with -q.

Other Benchmarking Tools

Tool Purpose Best For Platform
ONNX Runtime Cross-platform model benchmarking tiny ONNX models CPU, GPU, ARM
TensorFlow Lite Mobile-specific benchmarking Android, iOS edge models Mobile, Edge
PyTorch Benchmark Granular PyTorch model profiling Development-phase models Desktop
Edge Impulse Embedded ML benchmarking IoT and edge devices ARM Cortex, NVIDIA
DeepSparse CPU inference optimization Quantized ONNX models CPU
TensorRT GPU-optimized inference NVIDIA Jetson/GPUs GPU
Hugging Face Evaluate Multimodal task evaluation Vision-language tasks Cloud, Local
MLPerf Tiny Standardized tiny model benchmarks Embedded ML systems Multi-platform

References