Insights on edge AI testing, on-device performance, and CI/CD for Snapdragon deployments.
The EU's Digital Omnibus on AI entered into force July 27, 2026, pushing high-risk obligations from August 2026 to December 2027 (standalone systems) and August 2028 (AI in vehicles, medical devices, machinery). The reason for the delay is also the reason it doesn't buy you a pass on the evidence.
Read morearrow_forwardNVIDIA's new Jetson T3000 and T2000 modules put foundation models on mass-market robots for the first time. Its own case studies show teams shrinking memory footprint to hit a lower SKU — "without compromising performance." Measured how? Compute density solves cost. It doesn't answer whether the robot still behaves the same way after the change.
Read morearrow_forwardRunning an LLM on a phone is the easy part. Knowing it still behaves after quantization — and that it actually ran on the NPU, not silently on CPU — is what ships broken models. EdgeGate now runs a quantized LLM on real Snapdragon through Qualcomm's GenieX runtime, diffs it against a signed reference, and gates the build. Proven end-to-end on a Snapdragon X Elite.
Read morearrow_forwardGoogle's AI Edge Portal benchmarks LLM speed on 120+ Android phones — proof the category is real. But benchmarking answers "how fast," not "did the behaviour change" after a model, firmware, quantization, or chip update. That gap, on the robots and vehicles a phone farm never sees, is the one that ships broken models.
Read morearrow_forwardYour CI proves a model was good before it shipped. Nothing proves what it did after. Field Recorder is a black box for deployed edge AI — a tamper-evident, signed log of every on-device decision, with replay and EU AI Act Article 12 evidence.
Read morearrow_forwardHigh-risk AI obligations phase in through 2026–2027, with penalties up to €35M or 7% of global turnover. For on-device AI, the throughline is documented, reproducible evidence of accuracy and robustness — measured on the device, not in the cloud.
Read morearrow_forwardA teardown of a real Behavioral Gate run: a quantized LLM scored against a signed FP16 reference oracle on a physical Snapdragon 8 Gen 2, returning a signed, requirement-traced PASS — summary-only, no raw model output leaves the device.
Read morearrow_forwardA green checkmark proves a test ran. It does not prove what shipped to the device — which model, on which silicon, against which inputs, verifiable later. Why on-device AI needs signed, reproducible evidence.
Read morearrow_forwardA practical mapping of ISO 26262 Part 6 and Part 8 to a signed evidence bundle for on-device AI — configuration control, change management, verification with per-requirement ASIL traceability, and the honest limits of a verification tool.
Read morearrow_forwardONNX 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_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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