Edge ML: Practical Quantization, Pruning, and Latency Trade-Offs

Deploying machine learning models to microcontrollers and low-power embedded systems is fundamentally an exercise in resource accounting. Unlike server environments where compute is bounded by cost or thermal design power, edge devices operate under hard physical walls. An ARM Cortex-M4 or Cortex-M…

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1. 2026-10-22 13:49 · DEV Community — Machine Learning
   Edge ML: Practical Quantization, Pruning, and Latency Trade-Offs