Fitting AI Models To Edge Applications

Adapting artificial intelligence models for embedded devices requires establishing precise operational requirements before deployment. System architects typically deploy a combination of pruning, distillation, and quantization to shrink workloads without sacrificing performance. This coordinated compression approach is proving essential for running compact models and small language models directly on edge hardware.