Benchmark

You can easily measure the hardware performance of your model using the benchmarking feature in LaunchX. The benchmarking results can serve as a criterion for choosing the base model before training your AI model and help you select the best device optimized for your model.

Compatible model

Only single-input models are supported.
The four-dimensional array structure of images should be organized Batch, Number of Channels, Height, and Width.

Batch size: The number of combined input datasets that the model processes simultaneously.
Channel: 3 for RGB or BGR and 1 for Grayscale.
Input size: In computer vision tasks, input size refers to the size of the input images.

Supported Hardware

Arm MCU/NPU

Target Device	.tflite
Renesas RA8D1 (Arm Cortex-M85)	O (only INT8)
Renesas RA8D1 (Arm Cortex-M85) with helium	O (only INT8)
Alif Ensemble DevKit-E7 Gen2 (Arm Cortex-M55 + Ethos-U55)	O (only INT8)
Alif Ensemble DevKit-E7 Gen2 (Arm Cortex-M55 + Ethos-U55) with helium	O (only INT8)
Arm Virtual Hardware Corstone-300(Ethos-U55/U65)	O (only INT8)
Arduino Nicla Vision(Arm Cortex-M7/M4)	O (only INT8)
NXP i.MX 93(Arm Cortex-A55/M33+Ethos-U65)	O (only INT8)

NVIDIA

When benchmarking on Jetson, it is essential for the model file and target device to match the Jetpack version.

Target Device	.trt	.engine	.tflite	.onnx
Jetson Nano JetPack 4.4.1	O	O	X	O
Jetson Nano JetPack 4.6	O	O	X	O
Jetson Xavier NX JetPack 4.6	O	O	X	O
Jetson Xavier NX JetPack 5.0.2	O	O	X	O
Jetson TX2 JetPack 4.6	O	O	X	O
Jetson AGX Xavier JetPack 4.6	O	O	X	O
Jetson AGX Orin JetPack 5.0.1	O	O	X	O
Jetson Orin Nano JetPack 6.0	O	O	X	O
AWS-T4	O	O	X	O

Raspberry Pi

Target Device	.tflite	.onnx
Raspberry Pi ZeroW	O	X
Raspberry Pi Zero2W	O	X
Raspberry Pi 2B	O	O
Raspberry Pi 3B	O	O
Raspberry Pi 3B+	O	O
Raspberry Pi 4B	O	O
Raspberry Pi 5	O	O

Intel

Target Device	.zip(bin+xml)
Xeon W-2223	O