This article is providing benchmark of a set of well-known or reference pre-trained neural network models.
1. Benchmark results
2. Measure process
Only the machine learning inference is considered. In a complete application, the sensor acquisition, the data conditioning and pre-processing shall also be considered. The memory footprint are the one reported by X-CUBE-AI using the "Analyze" function (the version of X-CUBE-AI used is mentioned in the table). The input / output buffers are included, but the options have been selected allowing to overlay these buffers with the activations. The input / output buffer size are also reported. The inference time as well as the X-Cross error is the one reported by the "Validation on target". STM32Cube.AI is not modifying the DL/ML model topology. The impact on accuracy should be limited and the X-Cross error ensure that the difference... The validation can be done also with dataset... Quantized case through CLI scripts + data compression. When power measure is https://wiki.st.com/stm32mcu/wiki/AI:How_to_measure_machine_learning_model_power_consumption_with_STM32Cube.AI_generated_application