# Edge AI Hardware Optimization

## Docs

- [Config](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/api/config.md): Configuration management for edge AI optimization experiments
- [Data](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/api/data.md): Dataset loading and preprocessing utilities for edge AI training
- [Deploy](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/api/deploy.md): Deployment simulation tools for edge AI model inference
- [Experiments](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/api/experiments.md): Model training, hyperparameter sweeps, and Pareto frontier analysis for edge optimization
- [Hardware](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/api/hardware.md): Hardware profiling and analysis tools for edge AI optimization
- [Metrics](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/api/metrics.md): Performance metrics collection and evaluation functions for edge AI models
- [Model](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/api/model.md): Neural network model definitions and deterministic training utilities
- [Pruning](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/api/pruning.md): Structured channel pruning functions for reducing model size and complexity
- [Quantization](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/api/quantization.md): Model quantization functions for reduced precision inference
- [Reporting](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/api/reporting.md): Result aggregation and output generation for edge AI optimization studies
- [System Architecture](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/concepts/architecture.md): Understand the pipeline stages, design decisions, and operational constraints of the Edge AI Hardware Optimization framework
- [Hardware Constraints](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/concepts/hardware-constraints.md): Understand memory budgets, bandwidth utilization, and CPU frequency constraints for edge device deployment
- [Model Optimization](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/concepts/model-optimization.md): Explore structured pruning and quantization techniques for reducing model size and improving inference performance on edge devices
- [CPU Frequency Scaling](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/deployment/cpu-frequency-scaling.md): Simulate deployment on edge devices with variable CPU frequencies using latency multipliers
- [Memory Budget Constraints](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/deployment/memory-budgets.md): Learn how to enforce memory constraints and filter model variants that exceed device memory limits
- [Pareto Frontier Analysis](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/deployment/pareto-frontiers.md): Identify optimal model variants by computing Pareto frontiers for latency-accuracy and energy-accuracy tradeoffs
- [Benchmarking](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/guides/benchmarking.md): Measure and analyze model performance metrics for edge deployment optimization
- [Configuration](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/guides/configuration.md): Complete reference for configuring Edge AI Hardware Optimization parameters
- [Model Pruning](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/guides/pruning.md): Structured channel pruning for reducing model size and inference latency on edge devices
- [Model Quantization](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/guides/quantization.md): Reduce precision to FP16 or INT8 for faster inference and lower memory usage on edge devices
- [Bandwidth Utilization](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/hardware/bandwidth-utilization.md): Estimate achieved memory bandwidth and identify whether your edge model is compute-bound or memory-bound during inference
- [Layer-wise Analysis](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/hardware/layerwise-analysis.md): Analyze per-layer memory footprints, parameter sizes, and compute requirements to identify bottlenecks in your edge CNN models
- [Precision Tradeoffs](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/hardware/precision-tradeoffs.md): Compare accuracy, latency, memory, and energy across FP32, FP16, and INT8 precision modes to select optimal quantization strategies
- [Introduction](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/introduction.md): Reference pipeline for evaluating compact CNN deployments under edge-device constraints
- [Quickstart](https://mintlify.wiki/RaviTejaMedarametla/edge-ai-hardware-optimization/quickstart.md): Run the edge AI optimization pipeline in minutes and generate your first hardware-aware model analysis