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Edge AI Workload Design: Matching Models to Local Hardware

7 chapters · 31 lessons

1. 1. Hardware Constraints and Accelerator Architecture

   4 lessons

   Understand CPU, GPU, and NPU roles in edge inference and how memory, power, and thermal limits shape workload feasibility.

2. 2. Model Selection and Sizing for Edge Deployment

   5 lessons

   Learn to choose model architectures that fit device constraints while meeting quality and latency requirements.

3. 3. Inference Runtimes and Execution Paths

   4 lessons

   Map models to runtime frameworks and understand how software stacks route operations to hardware accelerators.

4. 4. Benchmarking and Profiling Real Performance

   5 lessons

   Measure actual inference latency, throughput, memory usage, and power draw under realistic workloads.

5. 5. Hybrid Inference Architecture Patterns

   4 lessons

   Design systems that intelligently route workloads between local accelerators and cloud services based on task characteristics.

6. 6. Workload Placement Decision Framework

   5 lessons

   Build a structured approach to decide which AI tasks should run locally, remotely, or in a hybrid configuration.

7. 7. Deployment and Monitoring in Production

   4 lessons

   Implement edge inference pipelines with versioning, telemetry, and graceful degradation for real-world reliability.