nRF Edge Impulse vs TFLite LiteRT TensorFlow Test

nRF Edge Impulse app is developed in collaboration with Edge Impulse to train and deploy embedded machine learning models on the Nordic Thingy:53 using Edge Impulse Studio. Every Nordic Thingy:53 comes pre-installed with firmware compatible with the nRF Edge Impulse mobile app. The app allows you to upload raw sensor data via your mobile device to the cloud-based Edge Impulse Studio and deploy fully trained ML models to the Nordic Thingy:53 over Bluetooth® Low Energy (LE). The app allows you to choose between existing projects in Edge Impulse studio data or creating a new project and can collect sensor data from the following set of sensors on the Nordic Thingy:53:  - Accelerometer  - Microphone  - Magnetometer  - Light  - Temperature, Pressure, Humidity and Air quality  - Inertial (Accelerometer and Magnetometer)  - Light and Environment  - Light and Inertial  - Environment and Inertial  - Light and Environment and Inertial  The collected sensor data can be labeled as training or testing data and may be inferenced directly in the app with machine learning model deployed to the Nordic Thingy:53.  A free Edge Impulse account, which may created from the app or at www.edgeimpulse.com, is required to connect nRF Edge Impulse with Edge Impulse studio.

How fast does your .tflite model actually run on this iPhone? Pick CPU, GPU or Neural Engine. Get real latency and QPS. No Mac, no Xcode project, no Bazel build, no cloud, no account. Open a .tflite file from the Files app, pick an accelerator, run it. A real measurement on the exact device you care about, in about a minute — no Bazel workspace, no benchmark tool built from source. PICK THE ACCELERATOR - Neural Engine — attaches the Core ML delegate, so supported operators run on the Apple Neural Engine - GPU — attaches the GPU delegate, backed by Metal - CPU — the plain interpreter, with a thread count you choose: 1, 2, 4, 6 or 8 One configuration per run: run it, change the delegate or thread count, load it again, compare the numbers yourself. WHAT YOU GET - Latency: mean, min and max, in milliseconds - Queries per second - Total queries completed and total run duration - A query count you set per run, from 50 to 5,000 invocations Mean, min and max time the model inference call only — no pre-processing, no post-processing. Duration covers the whole loop. The screen stays awake during a run. WHAT YOU SEE ABOUT THE MODEL - Every input and output tensor: index, name, shape and data type (uInt8, int32, float16, float32 and the rest) - Model file size and framework - Live app memory usage against the memory available to the app, refreshed while the model runs - A device tab with model identifier, system version, disk space, and the exact TensorFlow Lite runtime version this build links against A MobileNet model ships in the app and loads on launch, so you see a real measurement before importing your own. READ THIS BEFORE YOU TRUST A NUMBER Inference runs on a zeroed dummy input. The app fills every input tensor with zeros and invokes the model, so what you measure is the compute cost of the graph on the accelerator you picked — latency and throughput, nothing else. It is not an accuracy test. It will not tell you whether your model gives the correct answer, and it does not show output tensor values. Ask it "how fast", not "how correct". What it does not do, stated up front: no side-by-side accelerator comparison in one run, no per-layer or per-operator profiling, no result export, no image or real-data input. It opens .tflite files only. PRIVACY Your model never leaves the device. Loading, inspection, inference and timing all happen locally on your iPhone or iPad. No account, no sign-in — your models and your results are never uploaded. TensorFlow Lite is now called LiteRT; the format and the .tflite extension are unchanged. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc. Not affiliated with or endorsed by Google.