Lately, there has been a lot of talk regarding the possibility of machines learning to do what human beings do in factories, homes, and offices. With the advancement in artificial intelligence, there has been widespread fear and excitement about what AI, machine learning, and deep learning is capable of doing.
What is really cool is that Deep Learning and AI models are making their way from the cloud and bulky desktops to smaller and lower powered hardware. In this article, we will help you understand the strengths and weaknesses about three of the most dominant deep learning AI hardware platforms out there.
The Intel Movidius Neural Compute Stick (NCS) with Raspberry Pi
Developed by Intel Corporation, the Movidius Neural Compute Stick can efficiently operate without an active internet connection. Its computing capabilities come from the Myriad 2 Vision Processing Unit (VPU) . It offers profiling, tuning, and compiling a Deep Neural Network (DNN) on a development computer with the right tools.
The Intel NCS also offers prototyping, validation, and deployment of DNNs. Low power consumption is indispensable for autonomous and crewless vehicles as well as on IoT devices. The NCS is one of the most energy-efficient and low-cost USB stick for those looking to develop deep learning inference applications.
We can think of the Movidius NCS as a GPU (Graphics Processing Unit) packed inside a USB-stick. One can quickly run a trained model optimally on the unit, which one can use for testing purpose. Apart from this, the Movidius NCS offers the following features:
You can use it with Ubuntu version 16.04 or Raspberry Pi 3 Raspbian Stretch.
The Movidius NCS easily supports two DNN frameworks, namely TensorFlow and Caffe.
The Movidius Myriad 2 VPU works efficiently with Caffe-based Convolutional Neural Networks.
It can efficiently execute complex deep learning models, including SqueezeNet, GoogLeNet, Tiny YOLO, MobileNet SSD and AlexNet on systems with low processing power.
The Google Edge TPU (aka Google Coral)
On the one hand, Google Cloud TPU, also known as Google Coralwas developed for handling workloads more effectively than a GPU or CPU, it was limited for use to power server rooms and major data centers. Google also developed hardware for smaller devices, known as the Edge TPU. The Google EDGE TPU comes in a USB stick variant.
The Edge TPU is a small ASIC designed by Google for high performance ML inferencing on low-end devices. It can easily execute the latest mobile vision models including MobileNet V2 at 100+ fps. The Edge TPU supports TensorFlow Lite easily. The first-generation Edge TPU can easily execute deep feed forward neural networks (DFF) including convolutional neural networks (CNN), which makes it the best choice for different vision-based ML applications.
Google Edge TPU compliments the Cloud TPU and Google Cloud services to offer flawless end-to-end cloud-to-edge, hardware and software infrastructure for deploying customer's AI-based solutions.
The Edge TPU is not simply a piece of hardware. It easily combines the power of customized hardware, open software and state-of-the-art AI algorithms. It offers high-quality AI solutions.
Edge TPU can help grow many industrial-use cases including predictive maintenance, anomaly detection, robotics, machine vision, and voice recognition among others. It is useful for manufacturing, health care, retail, smart spaces, on-premise surveillance, and transportation sectors.
NVIDIA recently announced the sturdy developer board with Tegra SOC, the NVIDIA Jetson Nano. It is the best tool for designers and researchers to provide AI with an easy-to-use platform. The Jetson Nano also offers full software compatibility, not to forget the 472 GFLOPS of computing power combined with a quad-core 64-bit ARM CPU and 128-core integrated NVIDIA GPU. The Jetson also comes with an ample 4 Gigabytes of LPDDR4 memory with low-powered 5 and 10W power nodes.
The compatibility of NVIDIA Jetson NANO makes it easier to deploy AI-based workloads to Jetson. It can easily enable multi-sensor autonomous robots and advanced artificial intelligence systems. Apart from this, the NVIDIA Jetson offers the following advantages:
Storage and Connectivity
The Jetson Nano features robust eMMC storage and offers additional microSD card storage option. The connectivity on this developer kit features four USB 3.0 Type-A ports, an HDMI 2.0, Display Port 1.2, a 40-pin header, MIPI CSI camera connector, microSD slot, M.2 Wi-Fi slot, and Gigabit Ethernet. You would not get an integrated Wi-Fi onboard; however, the external card would make it easier to connect wirelessly.
Multi-Stream Video Analytics
The Jetson Nano can efficiently process eight full-HD motion video streams in real-time. It makes for the best low-powered intelligent video analytics platform for Network Video Recorders (NVR), smart cameras, and IoT gateways. The Jetson can quickly execute object detection in eight 1080p video streams with ResNet-based model running at high resolution with a minimal of 500 megapixels per second.
The Intel Movidius Neural Compute Stick (NCS) works efficiently, and is an energy-efficient and low-cost USB stick to develop deep learning inference applications. The Google Edge TPU offers high-quality AI solutions. Lastly, the NVIDIA Jetson Nano offers a lot of AI power in a small form factor. Hence, it depends on what type of applications is one willing to work on, which will decide what device would suit their needs.
Stay connected with latest AI & AR videos and tutorials!
Join our mailing list to receive the latest news and updates from our team. You'r information will not be shared.