What are GPUs?
Graphics Processing Unit or GPU is aprocessing module that works along with a CPU in computing systems.Traditionally, GPUs are used for graphics processing in display devices usedfor gaming, 3D rendering, etc.
In the year 2009, Andre Ng, who ispopular for his work in the deep learning space, with two other authors,published a research paper titled, “Large-scale Deep Unsupervised Learning using GraphicsProcessors.” From thispoint, the popularity of using GPUs for deep learning and other machinelearning & AI workloads has ever grown.
GPUs can perform parallel processingat a scale much better than traditional CPUs. The GPU parallel processingcapabilities are much necessary to bring down the time and computational costsrequired for developing deep learning and machine learning systems.
E2E GPU Instances
NVIDIA V100 and NVIDIA T4 GPUs powerE2E GPU Instances. NVIDIA V100 is known as the most advanced data center GPUever built. NVIDIA T4 is known for its inferencing capabilities. Both theprocessors are equipped with NVIDIA Tensor Cores.
Tensor Cores are specializedprocessing units that can do matrice multiply and accumulate results in asingle operation. Matrix operations are the heart of deep learning workloads,and Tensor Cores can provide the much-needed acceleration.
Machine learning is the ability ofmachines to learn from data and make decisions and again unlearn and relearnthrough the new data sets available. Machine learning is the building block ofmany technologies like recommendation systems, predictive analytics, dataanalytics, voice assistants, real-time fraud analysis, etc.
You can run all the popular machinelearning frameworks like TensorFlow, MxNet, PyTorch, Keras, among others, onE2E GPU Instances.
Deep learning is a subset of machinelearning — it uses artificial neural networks like convolutional layers, feed-forward,and recurrent neural networks. Typically, deep learning systems work withrelatively larger data sets and use matrix operations at the core.
NVIDIA T4 and NVIDIA V100 GPUs areequipped with Tensor Cores — which are specialized processing units in a GPUthat can perform matrix multiply and accumulate results in a single operation.This dramatically improves the performance of deep learning training andinference workloads. Click here to know more about how you can leverage Tensor Cores for your deeplearning workloads.
Image processing is an area of studywhere images are processed using deep neural networks to find usefulinformation. Image processing has huge applications in medical imaging whereimages can be processed by deep neural networks such as convolutional layers todetect disease, find cancer cells, among other applications.
Image processing is also employed infields like 4D Construction, Autonomous Vehicles, Fashion Assistants,Recreation of Ancient Paintings, etc.
Also known as video content analysis,video analytics is the application of automated video analysis to identifyobjects, people, and movements. Video analytics is applied in perimeterintrusion detection systems, crowd management, counterflow detection, andsuspect detection, among others. With smart cities highly-equipped with CCTVunits, automated video analysis is vital to prevent crimes and theft.
Natural Language Processing (NLP)
NLP is the analysis of naturallanguage either in text form or speech. NLP has a wide variety of applicationsin healthcare, diagnostics, sentimental analysis, analysis of financialmarkets, intelligent voice assistants, identifying fake news, legallitigations, talent recruitment, etc.
Click here to know more about E2E GPU Cloud powered by NVIDIA T4, NVIDIA A100 and NVIDIA V100.