Deep learning target detection models are often deployed on edge computing devices. What are the edge computing devices that are commonly used for deployment.

Problem description: Deep learning target detection models are often deployed on edge computing devices. What are the edge computing devices that are commonly used for deployment.

Questions and Answers:

Deploying deep learning target detection models on edge computing devices usually requires consideration of factors such as the device's computing power, memory, and power consumption. Here are some edge computing devices commonly used to deploy deep learning object detection models:

1. NVIDIA Jetson Series: NVIDIA’s Jetson Series are devices specifically designed for edge computing and embedded deep learning tasks. Models such as Jetson Nano and Jetson Xavier NX provide powerful GPU acceleration and are suitable for deploying deep learning target detection models.

2. Intel Movidius series: Intel's Movidius series includes processors such as the Myriad X and Myriad 2, which focus on vision and deep learning inference tasks. These processors can be integrated into various edge devices to provide efficient neural network inference capabilities.

3. Coral series: Google's Coral series includes hardware such as Edge TPU (Tensor Processing Unit), which is used to accelerate deep learning inference. Coral devices are suitable for embedded systems and some IoT devices, providing high performance and energy efficiency.

4. Raspberry Pi: Raspberry Pi is a low-cost embedded computing device suitable for some lightweight deep learning object detection models. Especially in Raspberry Pi 4 and above, its performance has been improved.

5. NXP i.MX series: NXP’s i.MX series is a family of embedded processors widely used in industrial, automotive and IoT applications. Some models have hardware acceleration suitable for deep learning tasks.

6. Xilinx series: Some Xilinx FPGA (Field Programmable Gate Array) and ACAP (Adaptive Compute Acceleration Platform) products can be used to accelerate deep learning inference and are suitable for some edge computing scenarios.

7. Huawei Ascend series: Huawei’s Ascend series includes NPU chips that focus on AI inference tasks. Some Huawei devices integrate Ascend chips for deep learning deployment at the edge.

When I read papers now, I see the first type the most.