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This article is providing benchmark of a set of well-known or reference pre-trained neural network models. Some STM32 results will be officially submitted to the TinyMLTM Perf benchmark from MLCommonsTM.

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	STM32 characterist.	Model Source	Flash Weights	RAM Buffers	Proc Time ms	Current (mA)	Energy (mJ) @ 3.3V	Version
STM32H723 NUCLEO-H723ZG	Flash 1MB RAM 564KB Freq 550MHz	mobilenet v1 0.25 128 quant source	500 KB	200 KB	10 ms	NA	NA	Cube AI v7.0.0 Cube IDE v1.7.0
STM32H723 NUCLEO-H723ZG	Flash 1MB RAM 564KB Freq 550MHz	Anomaly Detection v 0.5 TinyMLTM Perf	500 KB	200 KB	10 ms	NA	NA	Cube AI v7.0.0 Cube IDE v1.7.0
STM32H723 NUCLEO-H723ZG	Flash 1MB RAM 564KB Freq 550MHz	Anomaly Detection v 0.5 TinyMLTM Perf	500 KB	200 KB	10 ms	NA	NA	Cube AI v7.0.0 Cube IDE v1.7.0

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 clock source is always HSI and maximal frequency. Clock settings are configured automatically by X-CUBE-AI / STM32CUbeMX.

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