Intel Neural Compute Stick 2 Vs Gpu

Preparing the intel neural compute stick 2 and raspberry pi.

Intel neural compute stick 2 vs gpu. That shows not just in performance but in the wide. However it really struggles doing object detection at 11 fps. Both devices plug into a host computing device via usb. The most popular target for openvino is the movidius in the neural compute stick 2.

Setting up the intel neural compute stick 2 is very similar to the usb coral accelerator and again you can follow my instructions to. Intel neural computer stick 2 we ll just call it ncs2 here can perform 30 fps in classification using mobilenet v2 which is not bad. When running resnet 50 one of the most commonly used image recognition models it can only process 16 frames per second for image classification at a low resolution. The ncs2 uses a vision processing unit vpu while the coral edge accelerator uses a tensor processing unit tpu both of which are specialized processors for machine learning.

Since openvino s license is apache 2 0 third parties can add support for other hardware through a plug in. Intel neural compute stick 2 intel movidius myriad x vpu with asynchronous plug in enabled for 2xnce engines. A vpu is a visual processing unit or a processor that contains a neural compute engine. The nvidia jetson nano delivers 3 to 4 higher ai performance than platforms such as the intel neural compute stick 2 the nvidia jetson nano is high end high power hardware compared to movidius based the intel neural compute stick or the edgetpu based coral hardware from google.

As it just so happens you have multiple options from which to choose including google s coral tpu edge accelerator cta and intel s neural compute stick 2 ncs2. By the way ncs2 is a usb stick and it needs to use it together with an external host computer which is raspberry pi3 in this case.