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This article provides performance results for a set of well-known or reference pre-trained Neural Network models.

Performance metrics verified by the MLCommons association have been published in the MLPerf™ Tiny v1.1 benchmark. Below are additional performance metrics measured by STMicroelectronics, which have not been verified by MLCommons ^{[ST 1]}.

Information

X-CUBE-AI^{[ST 2]} is an expansion software for STM32CubeMX that generates optimized C code for STM32 microcontrollers and neural network inference. It is delivered under the Mix Ultimate Liberty+OSS+3rd-party V1 software license agreement^{[ST 3]} with the additional component license schemes listed in the product data brief^{[ST 4]}
The inference time, current and energy measurement process described is not done in a certified laboratory but can be reproduced by any user. The results are average values, which may vary depending on the input data (random data are currently used), the temperature, and the STM32 device itself.
Published data in this article is not contractual.
Copyright STMicroelectronics - All right reserved. Do not publish the following data without written consent of STMicroelectronics

1. Performance results

1.1. STM32 High Performance MCUs

STM32 High Performance MCUs inference time, memory footprint and energy at 3.3 V:

STM32 Board	STM32 characteristics	Model Source\Link	Flash total (Kbyte)	RAM total (Kbyte)	Proc Time (ms)	Cur. (mA)	Energy (mJ) 3.3 V	Version
STM32H735 SMPS STM32H735G-DK	Flash 1 Mbyte RAM 564 Kbytes (432) Freq 550 MHz	FDMobileNet 0.25 224x224x3 quant tfl [1]	188 Kbytes	166 Kbytes	43.3 ms	98 mA	14 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H735 SMPS STM32H735G-DK	Flash 1 Mbyte RAM 564 Kbytes (432) Freq 550 MHz	MobileNet v2 128x128x3 quant tfl	520 Kbytes	257 Kbytes	72 ms	101 mA	24 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H735 SMPS STM32H735G-DK	Flash 1 Mbyte RAM 564 Kbytes (432) Freq 550 MHz	Object Detector SSD MobileNet v1 0.25 192x192x3	544 Kbytes	298 Kbytes	118 ms	101 mA	39 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H735 SMPS STM32H735G-DK	Flash 1 Mbyte RAM 564 Kbytes (432) Freq 550 MHz	Yamnet 256 quant tfl	190 Kbytes	117 Kbytes	60 ms	103 mA	20.4 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H735 SMPS STM32H735G-DK	Flash 1 Mbyte RAM 564 Kbytes (432) Freq 550 MHz	CNN2D_ST_HandPosture VL53L8CX 8 postures	27 Kbytes	3.8 Kbytes	0.14 ms	98 mA	0.05 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H735 SMPS STM32H735G-DK	Flash 1 Mbyte RAM 564 Kbytes (432) Freq 550 MHz	HAR SVC float, onnx	128 Kbytes	6.3 Kbytes	0.75 ms	82 mA	0.21 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H735 SMPS STM32H735G-DK	Flash 1 Mbyte RAM 564 Kbytes (432) Freq 550 MHz	Anomaly Detection MLPerf™Tiny	280 Kbytes	6.39 Kbytes	0.9 ms	90 mA	0.26 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H735 SMPS STM32H735G-DK	Flash 1 Mbyte RAM 564 Kbytes (432) Freq 550 MHz	Key Word Spotting MLPerf™Tiny	68 Kbytes	24 Kbytes	9.1 ms	100 mA	3 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H735 SMPS STM32H735G-DK	Flash 1 Mbyte RAM 564 Kbytes (432) Freq 550 MHz	Image Classif. MLPerf™Tiny	123 Kbytes	49 Kbytes	26 ms	101 mA	8.6 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H735 SMPS STM32H735G-DK	Flash 1 Mbyte RAM 564 Kbytes (432) Freq 550 MHz	Visual Wake Word MLPerf™Tiny	99 Kbytes	56 Kbytes	16 ms	99 mA	5 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H747 SMPS STM32H747I-DISCO	Cortex^®-M7 Flash 2 Mbytes RAM 1 Mbyte (0.5) Freq 400 MHz⁽¹⁾	FDMobileNet 0.25 224x224x3 quant tfl [2]	188 Kbytes	166 Kbytes	60 ms	66 mA	13 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H747 SMPS STM32H747I-DISCO	Cortex^®-M7 Flash 2 Mbytes RAM 1 Mbyte (0.5) Freq 400 MHz⁽¹⁾	MobileNet v2 128x128x3 quant tfl	520 Kbytes	257 Kbytes	100 ms	67 mA	22 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H747 SMPS STM32H747I-DISCO	Cortex^®-M7 Flash 2 Mbytes RAM 1 Mbyte (0.5) Freq 400 MHz⁽¹⁾	Object Detector SSD MobileNet v1 0.25 192x192x3	544 Kbytes	298 Kbytes	165 ms	66 mA	36 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H747 SMPS STM32H747I-DISCO	Cortex^®-M7 Flash 2 Mbytes RAM 1 Mbyte (0.5) Freq 400 MHz⁽¹⁾	Yamnet 256 quant tfl	190 Kbytes	117 Kbytes	86 ms	66 mA	18.7 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H747 SMPS STM32H747I-DISCO	Cortex^®-M7 Flash 2 Mbytes RAM 1 Mbyte (0.5) Freq 400 MHz⁽¹⁾	CNN2D_ST_HandPosture VL53L8CX 8 postures	27 Kbytes	3.8 Kbytes	0.2 ms	64 mA	0.04 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H747 SMPS STM32H747I-DISCO	Cortex^®-M7 Flash 2 Mbytes RAM 1 Mbyte (0.5) Freq 400 MHz⁽¹⁾	HAR SVC float, onnx	128 Kbytes	6.3 Kbytes	1 ms	57 mA	0.19 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H747 SMPS STM32H747I-DISCO	Cortex^®-M7 Flash 2 Mbytes RAM 1 Mbyte (0.5) Freq 400 MHz⁽¹⁾	Anomaly Detection MLPerf™Tiny	280 Kbytes	6.39 Kbytes	1.22 ms	62 mA	0.24 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H747 SMPS STM32H747I-DISCO	Cortex^®-M7 Flash 2 Mbytes RAM 1 Mbyte (0.5) Freq 400 MHz⁽¹⁾	Key Word Spotting MLPerf™Tiny	68 Kbytes	24 Kbytes	12.7 ms	66 mA	2.7 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H747 SMPS STM32H747I-DISCO	Cortex^®-M7 Flash 2 Mbytes RAM 1 Mbyte (0.5) Freq 400 MHz⁽¹⁾	Image Classif. MLPerf™Tiny	123 Kbytes	50 Kbytes	36 ms	66 mA	7.8 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H747 SMPS STM32H747I-DISCO	Cortex^®-M7 Flash 2 Mbytes RAM 1 Mbyte (0.5) Freq 400 MHz⁽¹⁾	Visual Wake Word MLPerf™Tiny	99 Kbytes	56 Kbytes	22 ms	66 mA	4.7 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H7A3 SMPS NUCLEO-H7A3ZI-Q	Flash 2 Mbytes RAM 1.4 Mbyte (1.18) Freq 280 MHz	FDMobileNet 0.25 224x224x3 quant tfl [3]	188 Kbytes	166 Kbytes	85 ms	43 mA	12.6 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H7A3 SMPS NUCLEO-H7A3ZI-Q	Flash 2 Mbytes RAM 1.4 Mbyte (1.18) Freq 280 MHz	MobileNet v2 128x128x3 quant tfl	520 Kbytes	257 Kbytes	140 ms	45 mA	21 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H7A3 SMPS NUCLEO-H7A3ZI-Q	Flash 2 Mbytes RAM 1.4 Mbyte (1.18) Freq 280 MHz	Object Detector SSD MobileNet v1 0.25 192x192x3	544 Kbytes	298 Kbytes	232 ms	45 mA	34.4 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H7A3 SMPS NUCLEO-H7A3ZI-Q	Flash 2 Mbytes RAM 1.4 Mbyte (1.18) Freq 280 MHz	Yamnet 256 quant tfl	190 Kbytes	117 Kbytes	118 ms	45 mA	17.5 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H7A3 SMPS NUCLEO-H7A3ZI-Q	Flash 2 Mbytes RAM 1.4 Mbyte (1.18) Freq 280 MHz	CNN2D_ST_HandPosture VL53L8CX 8 postures	27 Kbytes	3.8 Kbytes	0.28 ms	43 mA	0.04 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H7A3 SMPS NUCLEO-H7A3ZI-Q	Flash 2 Mbytes RAM 1.4 Mbyte (1.18) Freq 280 MHz	HAR SVC float, onnx	128 Kbytes	6.3 Kbytes	1.4 ms	38 mA	0.18 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H7A3 SMPS NUCLEO-H7A3ZI-Q	Flash 2 Mbytes RAM 1.4 Mbyte (1.18) Freq 280 MHz	Anomaly Detection MLPerf™Tiny	280 Kbytes	6.39 Kbytes	1.8 ms	40 mA	0.2 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H7A3 SMPS NUCLEO-H7A3ZI-Q	Flash 2 Mbytes RAM 1.4 Mbyte (1.18) Freq 280 MHz	Key Word Spotting MLPerf™Tiny	68 Kbytes	24 Kbytes	18.1 ms	44 mA	2.7 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H7A3 SMPS NUCLEO-H7A3ZI-Q	Flash 2 Mbytes RAM 1.4 Mbyte (1.18) Freq 280 MHz	Image Classif. MLPerf™Tiny	124 Kbytes	49 Kbytes	52 ms	45 mA	7.7 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32H7A3 SMPS NUCLEO-H7A3ZI-Q	Flash 2 Mbytes RAM 1.4 Mbyte (1.18) Freq 280 MHz	Visual Wake Word MLPerf™Tiny	99 Kbytes	56 Kbytes	31 ms	45 mA	4.6 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1

⁽¹⁾ On Cortex^®-M7 core in SMPS mode 400 MHz instead of 480 max in LDO. The Cortex^®-M4 is running on a while(1) infinite loop.

For a given STM32 in a fixed configuration, the current consumption is in the same range regardless of the model. it might however vary depending on the complexity and topology of the model. The following table is providing the average current consumption of the model listed in the table above table (excluding the Anomaly Detection model which has a specific topology). These data can be used as a first estimation of the current consumption and the energy consumption of a new model from just the measurement of its inference time. From the average inference time of t second and the average current of i Ampere for a given input voltage of u Volt. The average energy is easily computed as (t x i x u) in Joule.

STM32 Board	STM32H735 550 MHz SMPS	STM32H747 400 MHz SMPS	STM32H7A3 280 MHz SMPS
Average current (mA)	97	65	43

STM32Cube.AI (X-CUBE-AI) can also generate a TensorFlow™ Lite for Microcontroller (TFLm) runtime implementation (based on TensorFlow™ version 2.10 sha-1 = 79f6defor STM32Cube.AI v8.1.0). The following table is comparing the TFLm runtime to the X-CUBE-AI runtime, the Flash and RAM footprints include the code / runtime footprint on top of the weights and activation buffer.

STM32 Board	STM32 characteristics	Model Source/Link	Runtime	Flash (Kbyte)	RAM (Kbyte)	Proc Time (ms)	Version
STM32H7A3 SMPS NUCLEO-H7A3ZI-Q	Flash 2 Mbytes RAM 1.4 Mbyte (1.18) Freq 280 MHz	Image Classif. MLPerf™Tiny	X-CUBE-AI	124 Kbytes	49 Kbytes	52 ms	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
		Image Classif. MLPerf™Tiny	TFLm	160 Kbytes	55 Kbytes	98 ms	TFLm sha-1 = 79f6de STM32CubeIDE 1.12.1
		Visual Wake Word MLPerf™Tiny	X-CUBE-AI	99 Kbytes	56 Kbytes	31 ms	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
		Visual Wake Word MLPerf™Tiny	TFLm	392 Kbytes	101 Kbytes	67 ms	TFLm sha-1 = 79f6de STM32CubeIDE 1.12.1

1.2. STM32 Ultra Low Power MCUs

STM32 Ultra Low Power MCUs inference time, memory footprint and energy at 3.3 V:

STM32 Board	STM32 characteristics	Model Source/Link	Flash Total. (Kbyte)	RAM Total. (Kbyte)	Proc Time (ms)	Cur. (mA)	Energy (mJ) 3.3 V	Version
STM32U585 SMPS NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	FDMobileNet 0.25 224x224x3 quant tfl [4]	187 Kbytes	166 Kbytes	209 ms	8.6 mA	6 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32U585 SMPS NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	MobileNet v2 128x128x3 quant tfl	520 Kbytes	257 Kbytes	362 ms	9.6 mA	11.4 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32U585 SMPS NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	Object Detector SSD MobileNet v1 0.25 192x192x3	544 Kbytes	298 Kbytes	587 ms	9.6 mA	18.6 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32U585 SMPS NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	Yamnet 256 quant tfl	190 Kbytes	117 Kbytes	300 ms	9.7 mA	9.6 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32U585 SMPS NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	CNN2D_ST_HandPosture VL53L8CX 8 postures	24 Kbytes	3.8 Kbytes	0.66 ms	9.3 mA	0.02 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32U585 SMPS NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	HAR SVC float, onnx	128 Kbytes	6.3 Kbytes	2.85 ms	9.6 mA	0.1 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32U585 SMPS NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	Anomaly Detection MLPerf™Tiny	280 Kbytes	6.39 Kbytes	4.9 ms	9 mA	0.14 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32U585 SMPS NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	Key Word Spotting MLPerf™Tiny	68 Kbytes	24 Kbytes	48.1 ms	9.6 mA	1.5 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32U585 SMPS NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	Image Classif. MLPerf™Tiny	123 Kbytes	50 Kbytes	134 ms	8.9 mA	3.8 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32U585 SMPS NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	Visual Wake Word MLPerf™Tiny	99 Kbytes	56 Kbytes	78.5 ms	9.7 mA	2.5 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32L4R5 LDO NUCLEO-L4R5ZI	Flash 2 Mbytes RAM 640 Kbytes Freq 120 MHz	FDMobileNet 0.25 224x224x3 quant tfl [5]	188 Kbytes	166 Kbytes	350 ms	24 mA	28 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32L4R5 LDO NUCLEO-L4R5ZI	Flash 2 Mbytes RAM 640 Kbytes Freq 120 MHz	MobileNet v2 128x128x3 quant tfl	520 Kbytes	257 Kbytes	592 ms	24 mA	47 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32L4R5 LDO NUCLEO-L4R5ZI	Flash 2 Mbytes RAM 640 Kbytes Freq 120 MHz	Object Detector SSD MobileNet v1 0.25 192x192x3	543 Kbytes	298 Kbytes	968 ms	24.4 mA	78 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32L4R5 LDO NUCLEO-L4R5ZI	Flash 2 Mbytes RAM 640 Kbytes Freq 120 MHz	Yamnet 256 quant tfl	188 Kbytes	117 Kbytes	488 ms	24.3 mA	39 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32L4R5 LDO NUCLEO-L4R5ZI	Flash 2 Mbytes RAM 640 Kbytes Freq 120 MHz	CNN2D_ST_HandPosture VL53L8CX 8 postures	24 Kbytes	3.8 Kbytes	1.3 ms	23.6 mA	0.1 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32L4R5 LDO NUCLEO-L4R5ZI	Flash 2 Mbytes RAM 640 Kbytes Freq 120 MHz	HAR SVC float, onnx	126 Kbytes	6.3 Kbytes	4.6 ms	26 mA	0.4 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32L4R5 LDO NUCLEO-L4R5ZI	Flash 2 Mbytes RAM 640 Kbytes Freq 120 MHz	Anomaly Detection MLPerf™Tiny	280 Kbytes	6.39 Kbytes	6.8 ms	23 mA	0.51 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32L4R5 LDO NUCLEO-L4R5ZI	Flash 2 Mbytes RAM 640 Kbytes Freq 120 MHz	Key Word Spotting MLPerf™Tiny	68 Kbytes	24 Kbytes	81 ms	24 mA	6.4 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32L4R5 LDO NUCLEO-L4R5ZI	Flash 2 Mbytes RAM 640 Kbytes Freq 120 MHz	Image Classif. MLPerf™Tiny	123 Kbytes	49 Kbytes	226 ms	24 mA	18 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32L4R5 LDO NUCLEO-L4R5ZI	Flash 2 Mbytes RAM 640 Kbytes Freq 120 MHz	Visual Wake Word MLPerf™Tiny	98 Kbytes	56 Kbytes	132 ms	24 mA	10.4 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32G474 LDO NUCLEO-G474REI	Flash 512 Mbytes RAM 128 Kbytes Freq 170 MHz	Anomaly Detection MLPerf™Tiny	280 Kbytes	6.39 Kbytes	5.15 ms	33 mA	0.56 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32G474 LDO NUCLEO-G474REI	Flash 512 Mbytes RAM 128 Kbytes Freq 170 MHz	Key Word Spotting MLPerf™Tiny	68 Kbytes	24 Kbytes	59 ms	35 mA	6.8 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32G474 LDO NUCLEO-G474REI	Flash 512 Mbytes RAM 128 Kbytes Freq 170 MHz	Image Classif. MLPerf™Tiny	123 Kbytes	49 Kbytes	161 ms	36 mA	19 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
STM32G474 LDO NUCLEO-G474REI	Flash 512 Mbytes RAM 128 Kbytes Freq 170 MHz	Visual Wake Word MLPerf™Tiny	99 Kbytes	56 Kbytes	95 ms	35 mA	11 mJ	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1

The following table is providing the average current consumption of the model listed in the table above table (excluding the Anomaly Detection model which has a specific topology). These data can be used as a first estimation of the current consumption and the energy consumption of a new model from just the measurement of its inference time. From the average inference time of t second and the average current of i Ampere for a given input voltage of u Volt. The average energy is easily computed as (t x i x u) in Joule

STM32 Board	STM32U585 160 MHz SMPS	STM32L4R5 120 MHz LDO Single Bank	STM32G474 170 MHz LDO
Average current (mA)	9.3	24.1	27.2

STM32Cube.AI (X-CUBE-AI) can also generate a TensorFlow™ Lite for Microcontroller (TFLm) runtime implementation (based on TensorFlow™ version 2.10 sha-1 = 79f6de for STM32Cube.AI v8.1.0). The following table is comparing the TFLm runtime to the X-CUBE-AI runtime, the Flash and RAM footprints include the code / runtime footprint on top of the weights and activation buffer.

STM32 Board	STM32 characteristics	Model Source/Link	Runtime	Flash (Kbyte)	RAM (Kbyte)	Proc Time (ms)	Version
STM32U585 SMPS NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	Image Classif. MLPerf™Tiny	X-CUBE-AI	123 Kbytes	49 Kbytes	134 ms	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
		Image Classif. MLPerf™Tiny	TFLm	161 Kbytes	55 Kbytes	251 ms	TFLm sha-1 = 79f6de STM32CubeIDE 1.12.1
		Visual Wake Word MLPerf™Tiny	X-CUBE-AI	99 Kbytes	56 Kbytes	78.5 ms	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
		Visual Wake Word MLPerf™Tiny	TFLm	393 Kbytes	101 Kbytes	176 ms	TFLm sha-1 = 79f6de STM32CubeIDE 1.12.1

2. SMPS vs LDO

Inference time, memory footprint and energy for SMPS and LDO power configuration at 3.3V :

STM32 Board	STM32 characteristics	Model Source/Link	PWR config	Cur. (mA)	Energy (mJ)	Proc Time (ms)	Version
STM32U585 NUCLEO-U575ZI-Q	Flash 2 Mbytes RAM 786 Kbytes Freq 160 MHz	Image Classif. MLPerf™Tiny	SMPS	8.9	3.8	134	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
		Image Classif. MLPerf™Tiny	LDO	19.2	8.5	134	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
		Visual Wake Word MLPerf™Tiny	SMPS	9.7	2.5	78.5 ms	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1
		Visual Wake Word MLPerf™Tiny	LDO	19.4	5	78.5 ms	STM32Cube.AI 8.1.0 STM32CubeIDE 1.12.1

3. Measurement process

On this performance only the Machine Learning model inference processing is reported. In a complete application, the sensor acquisition, the data conditioning and pre-processing must also be considered.

To give developers more control over their applications, ST introduced a new setting in STM32Cube.AI v8.1.0 to define priorities. If users choose the “Time” setting, the algorithm will take more RAM but have faster inference times. On the other hand, choosing “RAM” will have the smallest memory footprint and the slowest times. Finally, the default “Balanced” parameter finds the middle ground between the two approaches, providing a good compromise. For these measurements, the setting "balanced" is used.

The STM32 Board column indicates the STM32 reference and the board used for measurement. By default, the STM32 is configured in maximum performance configuration, so with maximum frequency and especially HCLK / AXI clock at maximal frequency. When a different setting is used it is specified (for instance lower frequency to use a different Voltage Scale or for STM32H7, lower HCLK/AXI frequency). Many STM32 embed a powerful switched-mode power supply (SMPS) that can be used to improve power efficiency when the supply voltage is high enough. When used instead of the integrated low-dropout regulator (LDO), power consumption is optimized by a factor equal to the ratio of the internal VCORE supply voltage to the VDD voltage. The improvement due to the SMPS depends only upon the SMPS efficiency and the VDD voltage. When SMPS is indicated it means that the internal voltage regulator used is the SMPS step-down converter instead of the LDO.

The STM32 Characteristics column provides the available internal Flash size, the full internal RAM size and the frequency. The RAM size includes the different kind of memories and banks, TCM, SRAM etc. For the time being, the buffers used by X-CUBE-AI must be placed in a continuous memory area, the maximal RAM size available in continuous area is provided between "()" if not equal to the full size. The frequency indicated is the operating frequency used for the test, so generally the maximal frequency. The only different case is with the STM32H747 Discovery kit (STM32H747I-DISCO), which is operating by default in SMPS power mode and therefore is limited to 400 MHz instead of 480 MHz. Data are rounded to 3 decimals.

The column Model Source/Links indicates the pre-trained ML model and the source, either how it was built / trained or where it can be downloaded. tfl stands for TensorFlow™ Lite .tflite model , h5 stands for Keras .h5 model, quant for quantized models on 8 bits.

The memory footprints 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 column Flash reports the Flash occupancy including the model weights, the runtime code generated by X-CUBE-AI to run the neural network and its constants (including the initialized tables).
The column RAM reports the RAM buffers occupancy, used to store the model activations as well as input and output buffers, and the RAM required by the runtime to inference the model. Note that to gain RAM space the "Use activation buffer for input buffer" and "Use activation buffer for the output buffer" options are selected (through X-CUBE-AI Advanced Settings panel).
For X-CUBE-AI runtime, the total Flash and RAM memory footprints are reported after an "Analyze" operation on the main panel by the fields Used Flash and Used RAM. The compiler used is gcc embedded in STM32CubeIDE.
For TensorFlow™ Lite for microcontroller runtime, the Flash and RAM memory footprints related to the runtime/code execution are computed from the memory map of the validation project of the given model built with STM32CubeIDE. The runtime/code part is computed taking into account all the modules used by tflite_micro. The STM32CubeIDE build options for TensorFlow™ Lite for microcontroller are the optimal ones (best compromise between speed and code size), -Ofast for GCC compiler and -Osize for G++ Compiler.

The column Proc Time reports the model inference processing time. When the current / energy is indicated, the measure is obtained through the X-CUBE-AI "System Performance" application following the process described on this WiKi article on power measurement. Otherwise the "Validation on target" application is used. In all cases, when generating the application, the selected clock source is always the HSI, X-CUBE-AI is generating first the optimal clock settings and eventually afterwards the clock is set to HSI. STM32CubeMX then autonomously reconfigures the clock settings.

Cur. and Energy is the current and energy computed following the process described in the WiKi article on power measurement. For STM32 Ultra Low Power microcontrollers, measurement is done with the X-NUCLEO-LPM01A power shield as described in the section 4.3.1 "Measure process when current is below 50 mA". For STM32 High Performance microcontrollers, measurement is done with the Qoitec Otii Arc power analyzer as described in the section 4.3.2 "Measure process when current is above 50 mA". In both cases, a 10 s window is used for averaging) and HSI is selected as the clock source.

Accuracy is not reported. X-CUBE-AI is not modifying the DL/ML model topology. The impact on accuracy should be limited. X-CUBE-AI is providing through the "Validation" application a way to measure the accuracy either on x86 or on the target. It can be used to check the eventual impact on accuracy. When running the "Validation on target" application several metrics are computed, one of them is the X-Cross providing error metrics between the original model executed in Python™ and the C model executed on the target. Random data can be used to compute the RMSE/MAE/L2R errors, however it is recommended to use true data to get the final accuracy. For more details on the metrics, refer to the X-CUBE-AI Embedded Documentation.

Note that accuracy check is important to compare a float model with a quantize model or when using the Weight compression feature of X-CUBE-AI for float models.

4. STMicroelectronics references

↑ MLPerf™ name and logo are trademarks of MLCommons Association in the United States and other countries. All rights reserved. Unauthorized use strictly prohibited. See www.mlcommons.org for more information.”
↑ X-CUBE-AI Expansion Package
↑ SLA0048 software license agreement
↑ DB3788 product data brief

STM32Cube.AI model performances

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