analiza aiqbench

The article covers using Qualcomm's AIMET toolkit to recover accuracy after model quantization, a standard step when shrinking neural networks for on-device inference. For ML engineers, the practical value is in the fine-tuning workflow for compressed models and how it trades accuracy against latency and memory on edge hardware. Its usefulness depends on how well the approach generalizes beyond Qualcomm's own targets.