This message will disappear after all relevant tasks have been resolved.

Semantic MediaWiki

There are 1 incomplete or pending task to finish installation of Semantic MediaWiki. An administrator or user with sufficient rights can complete it. This should be done before adding new data to avoid inconsistencies.

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.

1. Benchmark results

STM32	Board	Model	Source	Memory Config	Flash Weights	RAM Model	Inference Time	Current (mA)	Energy (mJ) @ 3.3V	Version	RAM Activations	RAM Input	RAM Output
STM32H723	STM32H723 DK	mobilenet	google	All internal	500 KB	200 KB	10 ms	NA	NA	X-CUBE-AI v7.0.0 STM32CubeIDE v1.7.0	200 kB	100 kB	3B
STM32H723	STM32H723 DK	mobilenet	google	All internal	500 KB	200 KB	10 ms	NA	NA	X-CUBE-AI v7.0.0 STM32CubeIDE v1.7.0	200 kB	100 kB	3B
STM32H723	STM32H723 DK	mobilenet	google	All internal	500 KB	200 KB	10 ms	NA	NA	X-CUBE-AI v7.0.0 STM32CubeIDE v1.7.0	200 kB	100 kB	3B

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.

RAM Model: buffers required to run the model, activations / input / output buffers with the "" option activated.

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

STM32Cube.AI model performances

1. Benchmark results

2. Measure process