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 intelligencethere 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. Developed by Intel Corporation, the Movidius Neural Compute Stick can efficiently operate without an active internet connection. 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.
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:. Google also developed hardware for smaller devices, known as the Edge TPU. It is the best tool for designers and researchers to provide AI with an easy-to-use platform. It can easily enable multi-sensor autonomous robots and advanced artificial intelligence systems.
Google Coral Edge TPU vs NVIDIA Jetson Nano: A quick deep dive into EdgeAI performance
The connectivity on this developer kit features four USB 3. You would not get an integrated Wi-Fi onboard; however, the external card would make it easier to connect wirelessly. The Jetson Nano can efficiently process eight full-HD motion video streams in real-time. The Jetson can quickly execute object detection in eight p video streams with ResNet-based model running at high resolution with a minimal of megapixels per second.
Hence, it depends on what type of applications is one willing to work on, which will decide what device would suit their needs. You're interested in deep learning and computer vision, but you don't know how to get started. Skip to content Blog. June 4, Ritesh. 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.With cool new hardware hitting the shelves recently, I was eager to compare performance of the new platforms, and even test them against high performance systems.
The Hardware. The Software. I will be using MobileNetV2 as a classifier, pre trained on the imagenet dataset. I use this model straight from Keras, which I use with TensorFlow backend. First, the model and an image of a magpie are loaded. I then execute 1 prediction as a warmup because I noticed the first prediction was always a lot slower then all the next ones.
I let it sleep for 1s, so that all threads are certainly finished. Then the script goes for it, and does classifications of that same image. By using the same image for all classifications, we assure that it will stay close to the databus throughout the test.
After all, we are interested in inference speeds, not the ability to load random data faster. Straight to the performance point. Straight away, there are 3 bars in the first graph that jump into view.
Let that sink in for a few seconds, and then prepare to be blown away, because that GTX draws a maximum of Wwhich is absolutely HUGE compared to the Corals 2. You managed to stand up again already? From a few years back, true, but still.Inside a Google data center
The Jetson Nano never could have consumed more then a short term average of But hey, I had the files ready anyway, and it was capable of running the tests, so more is always better right? Inference, yes, the Edge TPU is not able to perform backwards propagation. The logic behind this sounds more complex than it is though. Actually creating the hardware, and making it work, is a whole different thing, and is very, very complex.
But the logic functions are much simpler. Next image shows the basic principle around which the Edge TPU has been designed. A net like MobileNetV2 is consisting mostly of convolutions with activation layers behind.
A convolution is stated as :. That is exactly what the main component of the Edge TPU was meant for. Multiplying everything at the same time, then adding it all up at insane speeds. Sometimes rather complex to start with, but really really interesting! Why no 8-bit model for GPU? A GPU is inherently designed as a fine grained parallel float calculator.
So using floats is exactly what it was created for, and what it is good at. Why MobileNetV2? What else is available on the Edge TPU?Personally, I have a main focus on edge AI. With cool new hardware hitting the shelfs recently, I was eager to compare performance of the new platforms and even test them against high performance systems. I use this model straight from Keras, which I use with a TensorFlow backend.
First, the model and an image of a magpie are loaded. Then, we execute one prediction as a warmup because I noticed the first prediction was always a lot slower than the next ones and let it sleep for 1s, so that all threads are certainly finished. Then the script goes for it and does classifications of that same image.
By using the same image for all classifications, we assure that the data will stay close to the CPU throughout the test. After all, we are interested in inference speeds, not the ability to load random data faster. The scoring with the quantized tflite model for CPU was different, but it always seemed to return the same prediction as the others. Here are a few graphs, choose your favourite…. Straight away, there are 3 bars in the first graph that jump into view.
Yes, the first graph, linear scale fps, is my favourite, because it shows the difference in the high performance results. Let that sink in for a few seconds and then prepare to be blown away From a few years back, true, but still. The Jetson Nano never could have consumed more then a short term average of Not with the floating point model and still not really anything useful with the quantised model. But hey, I had the files ready anyway and it was capable of running the tests, so more is always better right?
Inference, yes, the Edge TPU is not able to perform backwards propagation. So training your model will still need to be done on a different preferably CUDA enabled machine. The logic behind this sounds more complex than it is though. Actually creating the hardware, and making it work, is a whole different thing, and is very, very complex. But the logic functions are much simpler.
Next image shows the basic principle around which the Edge TPU has been designed. A net like MobileNetV2 is consisting mostly of convolutions with activation layers behind. That is exactly what the main component of the Edge TPU was meant for. Multiplying everything at the same time, then adding it all up at insane speeds. There is no "CPU" behind this, it just does that whenever you pump data into the buffers on the left.
It's sometimes rather complex to start with, but really really interesting! A GPU is inherently designed as a fine grained parallel float calculator. Using floats is exactly what it was created for and what it's good at.
The Edge TPU has been designed to do 8-bit stuff and CPUs have clever ways of being faster with 8-bit stuff than full bitwitdh floats because they have to deal with this in a lot of cases. It used to be just MobileNet and Inception in their different versions, but as of the end of last week, Google pushed an update which allowed us to compile custom TensorFlow Lite models.Choosing the right type of hardware for deep learning tasks is a widely discussed topic.
An obvious conclusion is that the decision should be dependent on the task at hand and based on factors such as throughput requirements and cost. It is widely accepted that for deep learning training, GPUs should be used due to their significant speed when compared to CPUs. However, due to their higher cost, for tasks like inference which are not as resource heavy as training, it is usually believed that CPUs are sufficient and are more attractive due to their cost savings.
However, when inference speed is a bottleneck, using GPUs provide considerable gains both from financial and time perspectives. Expanding on this previous work, as a follow up analysis, here we provide a detailed comparison of the deployments of various deep learning models to highlight the striking differences in the throughput performance of GPU versus CPU deployments to provide evidence that, at least in the scenarios tested, GPUs provide better throughput and stability at a lower cost.
In our tests, we use two frameworks Tensorflow 1. We selected these models since we wanted to test a wide range of networks from small parameter efficient models such as MobileNet to large networks such as NasNetLarge. For each of these models, a docker image with an API for scoring images have been prepared and deployed on four different AKS cluster configurations:.
The CPU cluster was strategically configured to approximately match the largest GPU cluster cost so that a fair throughput per dollar comparison can be made between the 3 node GPU cluster and 5 node CPU cluster which is close but slightly more expensive at the time of these tests.
For more recent pricing, please use Azure Virtual Machine pricing calculator. The purpose was to determine if testing from different regions had any effect on throughput results. As it turns out, testing from different regions had some, but very little influence and therefore the results listed in this analysis only include data from the testing client in the East US region with a total of 40 different cluster configurations.
The tests were conducted by running an application on an Azure Windows virtual machine in the same region as the deployed scoring service. Using a range of concurrent threads, images were scored, and the results recorded was the average throughput over the entire set.
Google's Edge TPU Machine Learning Chip Debuts in Raspberry Pi-Like Dev Board
Actual sustained throughput is expected to be higher in an operationalized service due to the cyclical nature of the tests. The results reported below use the averages from the 50 thread set in the test cycle, and the application used to test these configurations can be found on GitHub. The following graph illustrates the linear growth in throughput as more GPUs are added to the clusters for each framework and model tested. Due to management overheads in the cluster, although there is a significant increase in the throughput, the increase is not proportional to the number of GPUs added and is less than percent per GPU added.
As stated before, the purpose of the tests is to understand if the deep learning deployments perform significantly better on GPUs which would translate to reduced financial costs of hosting the model. In the below figure, the GPU clusters are compared to a 5 node CPU cluster with 35 pods for all models for each framework. Note that, the 3 node GPU cluster roughly translates to an equal dollar cost per month with the 5 node CPU cluster at the time of these tests.
The results suggest that the throughput from GPU clusters is always better than CPU throughput for all models and frameworks proving that GPU is the economical choice for inference of deep learning models. In all cases, the 35 pod CPU cluster was outperformed by the single GPU cluster by at least percent and by the 3 node GPU cluster by percent which is of similar cost.
It is important to note that, for standard machine learning models where number of parameters are not as high as deep learning models, CPUs should still be considered as more effective and cost efficient. We hypothesize that this is because there is no contention for resources between the model and the web service that is present in the CPU only deployment.
It can be concluded that for deep learning inference tasks which use models with high number of parameters, GPU based deployments benefit from the lack of resource contention and provide significantly higher throughput values compared to a CPU cluster of similar cost. We hope that you find this comparison beneficial for your next deployment decision and let us know if you have any questions or comments.
Blog Data Science. GPU vs CPU results As stated before, the purpose of the tests is to understand if the deep learning deployments perform significantly better on GPUs which would translate to reduced financial costs of hosting the model.Wednesday September 25, It is able to provide real-time image classification or object detection performance while simultaneously achieving accuracies typically seen only when running much larger, compute-heavy models in data centers.
In this article, we provide an overview of the EdgeTPU and our web-based retraining system that allows users with limited machine learning and AI expertise to build high-quality models for on Ohmni. The CPU is a general-purpose processor based on the von Neumann architecture.
The main advantage of a CPU is its flexibility. With its Von Neumann architecture, we can load any kind of software for millions of different applications. This mechanism is the main bottleneck of CPU architecture. Meanwhile, the GPU Figure 1 architecture is designed for applications with massive parallelism, such as matrix calculations in deep learning models. The modern GPU typically has 2,—5, ALUs in a single processor which means you can execute thousands of computations simultaneously.
However, the GPU is still a general-purpose processor that has to support a wide range of applications. For every single calculation in the thousands of ALUs, the GPU needs to access registers or shared memory to read and store the intermediate calculation results. Because the GPU performs more parallel calculations on its thousands of ALUs, it also spends proportionally more energy accessing memory and also increases the footprint of the GPU for complex wiring.
An alternative to the general-purpose processor is the TPU, illustrated in Figure 2. It was designed by Google with the aim of building a domain-specific architecture.
In particular, the TPU is specialized for matrix calculations in deep learning models by using the systolic array architecture. Because the primary task for this processor is matrix processing, hardware designers of the TPU know every calculation to perform that operation.
They can place thousands of multipliers and adders and to connect them directly to form a large physical matrix of those operators. Therefore, during the whole process of massive calculations and data passing, no memory access is required at all.
For this reason, the TPU can achieve high computational throughput on deep learning calculations with much less power consumption and a smaller footprint. The main benefit of running code in the cloud is that we can assign the necessary amount of computing power for that specific code. In contrast, running code on the edge means that code will be on-premise.
In this case, users can physically touch the device on which the code runs. The primary benefit of this approach is that there is no network latency. This lack of latency is great for IoT and robotics-based solutions that generate a large amount of data.
Users of Ohmni Developer Edition can request this additional hardware customization and then train their own deep learning models that support EdgeTPU to deploy to Ohmni. However, training the models requires advanced knowledge of machine learning and AI. To facilitate the users in building AI applications on Ohmni, we developed a web-based retraining system for EdgeTPU models, which can support the users in training high-quality deep learning models with minimal effort and machine learning expertise.
Figure 4 depicts an overview of our retraining system. To create a custom model for EdgeTPU, users upload their training images and the corresponding annotations to our retraining server via a web browser. For example, to train an object detection model, the training data consists of a set of images and a set of annotation files that contain the coordinates of a bounding box for all objects in the images Figure 5. The larger the amount of training data, the better the performance of the resulting model.
Figure 5: An example of training data for an object detection model.AI is pervasive today, from consumer to enterprise applications. It delivers high performance in a small physical and power footprint, enabling the deployment of high-accuracy AI at the edge. Thanks to its performance, small footprint, and low power, Edge TPU enables the broad deployment of high-quality AI at the edge. Edge TPU isn't just a hardware solution, it combines custom hardware, open software, and state-of-the-art AI algorithms to provide high-quality, easy to deploy AI solutions for the edge.
Edge TPU can be used for a growing number of industrial use-cases such as predictive maintenance, anomaly detection, machine vision, robotics, voice recognition, and many more. It can be used in manufacturing, on-premise, healthcare, retail, smart spaces, transportation, etc. The Edge TPU allows you to deploy high-quality ML inferencing at the edge, using various prototyping and production products from Coral.
In addition to its open-source TensorFlow Lite programming environment, the Coral platform provides a complete developer toolkit so you can compile your own models or retrain several Google AI models for the Edge TPU, combining Google's expertise in both AI and hardware. Products listed on this page are in beta. For more information on our product launch stages, see here. Why Google close Groundbreaking solutions.
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Enroll in on-demand or classroom training. Get Help from an Expert Consulting.Google began using TPUs internally inand in made them available for third party use, both as part of its cloud infrastructure and by offering a smaller version of the chip for sale.
Google's TPUs are proprietary. Some models are commercially available, and on February 12,The New York Times reported that Google "would allow other companies to buy access to those chips through its cloud-computing service.
Google’s Edge TPU. What? How? Why?
It is also used in RankBrain which Google uses to provide search results. Compared to a graphics processing unitit is designed for a high volume of low precision computation e.
The second-generation TPU was announced in May This makes the second-generation TPUs useful for both training and inference of machine learning models. The third-generation TPU was announced on May 8, The Edge TPU also only supports 8-bit math, meaning that for a network to be compatible with the Edge TPU, it needs to be trained using TensorFlow quantization-aware training technique.
Google’s Edge TPU Debuts in New Development Board
From Wikipedia, the free encyclopedia. Redirected from Tensor Processing Unit. This article is about the tensor processing unit developed by Google. For other devices that provide tensor processing for artificial intelligence, see AI accelerator.
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