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Edge AI Hardware Optimization A reference pipeline for evaluating compact CNN deployments under edge-device constraints. Optimize models through pruning, quantization, and hardware-aware analysis.
Quick Start Get up and running with the optimization pipeline in minutes
Install dependencies
Create a virtual environment and install the required packages: python -m venv .venv source .venv/bin/activate pip install -r requirements.txt
The pipeline requires PyTorch, torchvision, matplotlib, pandas, PyYAML, ONNX, and ONNXRuntime.
Configure your experiment
Set the Python path and review the default configuration: The configs/default.yaml file contains deterministic baseline settings including pruning levels, precision modes, and memory budgets.
View default configuration
seed : 7 pruning_levels : [ 0.0 , 0.25 , 0.5 , 0.7 ] precisions : [ fp32 , fp16 , int8 ] memory_budgets_mb : [ 1.0 , 2.0 , 4.0 ] active_memory_budget_mb : 2.0
Run the pipeline
Execute the complete optimization pipeline: python scripts/run_pipeline.py --config configs/default.yaml
This will train a baseline CNN, sweep through pruning and precision variants, and generate Pareto frontiers.
Analyze results
The pipeline generates comprehensive outputs in the outputs/ directory:
sweep_results.csv — All model variants with metricspareto_frontier_latency.csv — Optimal latency-accuracy tradeoffspareto_frontier_energy.csv — Optimal energy-accuracy tradeoffshardware_summary.csv — Bandwidth utilization and compute estimatesVisualization plots for accuracy vs latency, energy, and memory
For production-grade claims, run multiple seeds and aggregate results externally for statistical confidence.
Key Features Hardware-aware optimization tools for edge AI deployment
Structured Pruning Remove whole channels from convolutional layers to reduce model size while preserving dense kernel compatibility.
Multi-Precision Support Evaluate FP32, FP16, and INT8 variants with calibration-based quantization for optimal performance.
Memory Budget Constraints Enforce SRAM-style memory limits and filter infeasible candidates before Pareto analysis.
Pareto Frontier Analysis Generate optimal tradeoff curves for latency-accuracy and energy-accuracy to guide deployment decisions.
Layer-wise Profiling Analyze activation memory, parameter footprints, and MAC operations per layer to identify bottlenecks.
Deterministic Benchmarking Reproducible latency measurements with configurable benchmark windows and statistical reporting.
Explore by Topic Deep dive into optimization techniques and hardware analysis
Architecture Understand the pipeline stages from configuration to Pareto frontier generation.
Model Optimization Learn how pruning and quantization affect model accuracy and resource usage.
Hardware Constraints Explore memory budgets, bandwidth utilization, and CPU frequency scaling.
Configuration Guide Customize experiment parameters including datasets, batch sizes, and benchmarking settings.
Bandwidth Utilization Estimate achieved bandwidth and identify compute vs transfer bottlenecks.
Precision Tradeoffs Compare mean accuracy, latency, and memory across FP32, FP16, and INT8 modes.
Ready to optimize your models? Start with the quickstart guide to run your first optimization sweep, or explore the API reference to integrate the pipeline into your workflow.