What is the difference between Intel NCS and NCS2?
Inference time is the time of objects being detected in a single frame and the CPU usage represents the co-processor utilization. Intel NCS has a lower frame rate however the second generation, Intel NCS2 is claimed to offer up to 8X faster performance [ 2 ]. Detection results represent the confidence score of the detection.
How many VPUs does the NCS2 have?
The NCS2 is equipped with 16 VPU’s or Visual Processing Units which are designed specifically for image and video processing. With this it’s possible to run Machine Learning frameworks like Caffe and Tensorflow and to leverage CNN (Convolutional Neural Networks) to do inferencing on data.
What is the impact of NCS2 on edge computing?
The NCS2 has the potential to make Edge Computing a reality instead of just a buzzword. The compound impact could be significant if the network backhaul is taken into consideration. Imagine replacing a constant stream of video data across the network with just the metadata resulting from running inferencing on the very same video stream.
Does the NCS2 get hot or cold?
The NCS2 is passively cooled and actually is its own heatsink. It’s made of metal and the “fins” on both sides of it allow enough airflow to keep it cool. It does get warm during testing but so far not extremely so. 6. Size
What is movidius ncs2?
A Plug and Play Development Kit for AI Inferencing. Build and scale with exceptional performance per watt per dollar on the Intel® Movidius™ Myriad™ X Vision Processing Unit (VPU) Start developing quickly on Windows® 10, Ubuntu*, or macOS*
What does Intel neural compute stick 2 do?
Intel Neural Compute Stick 2 (NCS 2) is a development and prototyping platform. You can use it to quickly bring computer vision and AI to IoT and edge devices. Intel NCS 2 is built on the latest Intel Movidius Myriad X VPU (visual processing unit), a dedicated hardware accelerator for deep neural-network inferences.
What does Intel neural compute stick do?
The Movidius Neural Compute Stick enables rapid prototyping, validation and deployment of Deep Neural Network (DNN) inference applications at the edge. Its low-power VPU architecture enables an entirely new segment of AI applications that aren't reliant on a connection to the cloud.
What is neural stick?
Intel has launched the next version of its Neural Compute Stick, a simple USB device that the company says can greatly accelerate AI processing and deep learning inference on consumer PCs. It is aimed at use cases where neural networks need to be deployed without a connection to cloud-based computing resources.
How fast is Intel neural compute stick?
4 trillion operations-per-secondBased on the maximum performance of operations-per-second across all available compute units, the Intel Neural Compute Stick 2 achieves a total performance of over 4 trillion operations-per-second.
How does OpenVINO work?
OpenVINO is a cross-platform deep learning toolkit developed by Intel. The name stands for “Open Visual Inference and Neural Network Optimization.” OpenVINO focuses on optimizing neural network inference with a write-once, deploy-anywhere approach for Intel hardware platforms.
What is ML accelerator?
These so-called machine learning accelerators (also called AI accelerators) have the potential to greatly increase the efficiency of ML tasks (usually deep neural network tasks), for both training and inference.
What are the different AI hardware platforms available?
Most Popular Edge AI Hardware Accelerators1.) VPU: Vision Processing Unit. Vision Processing Units allow demanding computer vision and edge computing AI workloads to be conducted with high efficiency. ... 2.) GPU: Graphics Processing Unit. ... 3.) TPU: Tensor Processing Unit.
How do you use movidius neural compute stick?
The NCSDK is required to interact with the Movidius stick. The goal of the SDK is to provide an interface to neural compute hardware....Install NCSDKNavigate to the NCSDK directory in Console.Build the SDK in Console: make install.Build the examples: make examples.
What is OpenVINO toolkit?
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks. Use models trained with popular frameworks like TensorFlow, PyTorch and more.
What is ML accelerator?
These so-called machine learning accelerators (also called AI accelerators) have the potential to greatly increase the efficiency of ML tasks (usually deep neural network tasks), for both training and inference.
What is OpenVINO toolkit?
OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference. Boost deep learning performance in computer vision, automatic speech recognition, natural language processing and other common tasks. Use models trained with popular frameworks like TensorFlow, PyTorch and more.
What are the different AI hardware platforms available?
Most Popular Edge AI Hardware Accelerators1.) VPU: Vision Processing Unit. Vision Processing Units allow demanding computer vision and edge computing AI workloads to be conducted with high efficiency. ... 2.) GPU: Graphics Processing Unit. ... 3.) TPU: Tensor Processing Unit.
How much does the NCS2 cost?
The NCS2 isn’t a very expensive device at $87.99 USD (Amazon.com at the time of writing). This is a pretty cheap way to add Machine Learning power on devices which have lower-end CPUs, like IoT edge gateways. There are slight differences in results when the NCS2 is running on less powerful platforms vs. newer machines.
Can you use the NCSDK2 with the NCS2?
The NCSDK and NCSDK2 can’t be used. There are two versions of the NCSDK available. Both of these are for the original NCS and won’t work with the NCS2. This is clearly stated on the Intel webpage but if you’re like me you may miss it and make the assumption that the NCSDK2 is for the NCS2 stick.
Can the Movidius NCS2 compete with the i7?
From the results it’s clear that the Movidius NCS2 can’t compete with a modern i7 CPU, but of course it’s a lot cheaper and supposedly draw a lot less power. That would make it ideal for connecting to edge devices where limiting power consumption may be desired.
Is the NCS2 running on a newer machine?
There are slight differences in results when the NCS2 is running on less powerful platforms vs. newer machines. This indicates that there are more factors that play into the results than the NCS2 itself, like the type of CPU, memory and storage on the platform the NCS2 is plugged into.
Does the NCS2 support FP16?
This is because the NCS2 only support half precision (FP16) whereas the CPU only support full, or normal, precision (FP32). This probably doesn’t make a huge difference when doing inferencing, which is the only thing the NCS2 is likely to be doing in a real-world application.
Is Movidius Neural Compute Stick 2 good?
The Intel Movidius Neural Compute Stick 2 does seem like a valid option for running inferencing at the edge. While it’s not as powerful as a full-on GPU nor a modern CPU, it has the potential to excel in the niche of low-power edge devices like IoT gateways where the onboard CPU isn’t powerful enough to do inferencing on its own.
Is the NCS2 a Gen4 or Gen8?
This is where it was tested initially. Further testing shows that it’s more or less equal to a Gen4 Intel i7 but falls behind when compared to a Gen8 Intel i7 CPU. This is expected of course.
How much does the NCS2 cost?from jonamiki.com
The NCS2 isn’t a very expensive device at $87.99 USD (Amazon.com at the time of writing). This is a pretty cheap way to add Machine Learning power on devices which have lower-end CPUs, like IoT edge gateways. There are slight differences in results when the NCS2 is running on less powerful platforms vs. newer machines.
Can you use the NCSDK2 with the NCS2?from jonamiki.com
The NCSDK and NCSDK2 can’t be used. There are two versions of the NCSDK available. Both of these are for the original NCS and won’t work with the NCS2. This is clearly stated on the Intel webpage but if you’re like me you may miss it and make the assumption that the NCSDK2 is for the NCS2 stick.
Can the Movidius NCS2 compete with the i7?from jonamiki.com
From the results it’s clear that the Movidius NCS2 can’t compete with a modern i7 CPU, but of course it’s a lot cheaper and supposedly draw a lot less power. That would make it ideal for connecting to edge devices where limiting power consumption may be desired.
Is the NCS2 running on a newer machine?from jonamiki.com
There are slight differences in results when the NCS2 is running on less powerful platforms vs. newer machines. This indicates that there are more factors that play into the results than the NCS2 itself, like the type of CPU, memory and storage on the platform the NCS2 is plugged into.
Is the NCS2 a Gen4 or Gen8?from jonamiki.com
This is where it was tested initially. Further testing shows that it’s more or less equal to a Gen4 Intel i7 but falls behind when compared to a Gen8 Intel i7 CPU. This is expected of course.
Does the NCS2 support FP16?from jonamiki.com
This is because the NCS2 only support half precision (FP16) whereas the CPU only support full, or normal, precision (FP32). This probably doesn’t make a huge difference when doing inferencing, which is the only thing the NCS2 is likely to be doing in a real-world application.
Is Movidius Neural Compute Stick 2 good?from jonamiki.com
The Intel Movidius Neural Compute Stick 2 does seem like a valid option for running inferencing at the edge. While it’s not as powerful as a full-on GPU nor a modern CPU, it has the potential to excel in the niche of low-power edge devices like IoT gateways where the onboard CPU isn’t powerful enough to do inferencing on its own.
What is the difference between Nvidia Jetson Nano and Intel NCS?
Nvidia Jetson Nano is an evaluation board whereas Intel NCS and Google Coral are more of the add-on devices that can be attached to existing hardware. The PoC could be rapidly developed using the aforementioned hardware.
Does Nvidia Jetson Nano require a host computer?
The cost of hardware accelerators together with the required components is given in the following table. It is worth mentioning that Nvidia Jetson Nano is a developing board and works as a standalone device. However, Google Coral USB and Intel NCS require a host computer for handling the data streams. The host computer could be a single board computer such as Raspberry Pi or any other x86 computer with either Windows or Linux operating systems. The cost in the following table is calculated with Raspberry Pi 3 B+.