Insights on edge AI testing, on-device performance, and CI/CD for Snapdragon deployments.
ONNX Runtime's QNN execution provider silently routes unsupported ops to the CPU. Eval passes, production latency triples. Here's the three CI assertions that catch it before merge.
Read morearrow_forward1/ Reasoning distillation is quietly 2026's most important AI trend. o3-level reasoning compressing onto Snapdragon hardware. Not 2027. Now.
Read morearrow_forwardComplete walkthrough for connecting your ML workflow to Qualcomm AI Hub via EdgeGate — API setup, model submission, pipeline creation, and GitHub Actions integration.
Read morearrow_forwardFP32 to INT8 quantization can silently break your model on real hardware. Learn how to test for accuracy drift, operator fallback, memory spikes, and latency regressions.
Read morearrow_forwardWe ran MobileNetV2 and ResNet50 100 times each on a Samsung Galaxy S24. Here's the raw data on variance, cold starts, and why median-of-N gating catches what single-run testing misses.
Read morearrow_forwardEdge AI regressions cost teams weeks of debugging. Learn why optimized models break on real Snapdragon hardware and how automated quality gates prevent production failures.
Read morearrow_forwardEdge AI inference isn't perfectly repeatable. Learn how median-of-N gating, warmup exclusion, and flake detection bring statistical rigor to hardware testing.
Read morearrow_forwardCryptographically signed evidence bundles give release engineers proof that every model passed quality gates on real hardware. Here's how they work.
Read morearrow_forwardYour model hits 12ms inference in the cloud. On a Snapdragon 8 Gen 3, it takes 47ms. Here's why the gap exists and what to do about it.
Read morearrow_forwardEmulators miss thermal throttling, firmware quirks, and quantization drift. Learn how hardware-in-the-loop testing catches failures before production.
Read morearrow_forwardStep-by-step guide to adding regression gates that test your models on real Snapdragon hardware in every pull request.
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