Available Targets

Here is the list of all the available targets of the dAIEdge-VLab and their inference engine(s).

Last update : 10:03:2025 at 07:24:40

Target Name	runtime(s)	Description
STM32MP257	tflite	The STM32MP257 offers advanced edge AI capabilities with its NPU accelerator and provides flexibility in running AI applications on either the CPU, GPU, or NPU. Note that the benchmarks performed by the dAIEdge-VLab are run on the NPU.
Raspberry Pi 4 B	ort tflite	Raspberry Pi 4b features theBroadcom BCM2711, Quad core Cortex-A72 (ARM v8) 64-bit SoC @ 1.8GHz. The available Raspberry Pi 4b has 4G of RAM.
STM32L4R9	tflite x-cube-ai	STM32L4R9 device extend the ultra-low-power portfolio and performance with an Arm® Cortex®-M4 core with DSP and floating-point unit (FPU) at 120 MHz.
Jetson Orin Nano	ort trt	Jetson Orin Nano Developer Kit is an official NVIDIA development kit based on the Jetson Orin Nano 8GB module that delivers up to 40 TOPS of AI performance.
LPC55S69JBD100	tflite	High Efficiency Arm® Cortex®-M33-Based Microcontroller from NXP.
Qualcomm RB3 Gen 2	tflite	The Qualcomm RB3 Gen 2, based on the QCS6490 processor, is an IoT development kit designed for high-performance computing.
Raspberry Pi 5	ort tflite	Raspberry Pi 5 features the Broadcom BCM2712 quad-core Arm Cortex A76 processor @ 2.4GHz, making it up to three times faster than the previous generation. The available Raspberry Pi 5 has 4G of RAM.
Myriad X	Rupia	The Intel® Movidius™ Myriad™ X VPU can reach up to 105 FPS (80 typical), and can perform over 1 trillion floating point operations per second as a dedicated neural network accelerator. The Intel® Movidius™ Myriad™ X is an ultra-low power VPU.

Configurations

Here is the list of all the available configurations that can be used with the dAIEdgeVLab Python API. The values in the column Target msut be used for the parameter target in the startBenchmark methode. Same for the parameter runtime.

ID	Target name	`Target`	`Runtime`
1	STM32MP257	stm32mp257	tflite
2	Raspberry Pi 4 B	rpi4b	ort
3	Raspberry Pi 4 B	rpi4b	tflite
4	STM32L4R9	stm32l4r9	tflite
5	STM32L4R9	stm32l4r9	x-cube-ai
6	Jetson Orin Nano	jetsonorinnano	ort
7	Jetson Orin Nano	jetsonorinnano	trt
8	LPC55S69JBD100	lpc55s69jbd100	tflite
9	Qualcomm RB3 Gen 2	qualcommrb3gen2	tflite
10	Raspberry Pi 5	rpi5	ort
11	Raspberry Pi 5	rpi5	tflite
12	Myriad X	xe2	Rupia

Use example

Here is an example for the configuration 2:

api = dAIEdgeVLabAPI("setup.yaml")

# Start a benchmark for a given target, runtime and model
benchmark_id = api.startBenchmark(
    target = "rpi4b", 
    runtime = "ort", 
    model_path = MODEL
    )