STM32Cube.AI model performances

This article is providing benchmark of a set of well-known or reference pre-trained neural network models.

Information

STM32Cube.AI is a software aiming at the generation of optimized C code for STM32 and neural network inference. It is delivered under the Mix Ultimate Liberty+OSS+3rd-party V1 software license agreement (SLA0048). Inference time, current and energy measures process is described, not done in a certified laboratory but can be reproduce by any user. The results are average values and will vary depending on the input data (random data are currently used), temperature and the STM32 device itself. Published data on this article are not contractual.

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