Why GPU Can Process Image Much Faster than CPU?

February 12, 2021

Graphical Processing Unit (GPU) and Central Processing Unit (CPU) have many commonalities amongst them and, at the same time, have significant differences when it comes to their roles and characteristics. Technical advancements have offered GPU capabilities to compete with the established players like CPUs for making them ideal for a plethora of applications such as fast image processing.

This blog throws light on GPUs' abilities and CPUs for fast image processing and the benefits and reasons why GPUs have the upper hand over CPU-based solutions.

Before going into the detail, let’s understand what CPUs and GPUs are, along with the critical aspects of fast image processing.

What Are CPUs?

CPU is often referred to as the heart or brain of a computer and is responsible for running most of the software. Simultaneously, specific applications, such as image processing, can be overwhelming for a CPU to manage. A GPU is designed to take care of such applications.

What Are GPUs?

A GPU is specially designed for tasks like quick image rendering. This specialized type of microprocessors can respond to graphically intense applications that can drain the CPU and degrade its performance. Although initially designed to offload image processing related tasks from CPUs, modern technology has offered today’s GPUs the capability to perform rapid mathematical operations for many other applications besides rendering.

Vital Aspects of Fast Image Processing Algorithms

Fast image processing algorithms have specific vital characteristics such as parallelization, locality, simplicity, and how they help GPUs offer superior performance than CPUs.

  • Parallelization Potential – Tasks can be processed in parallel as every pixel doesn’t depend on the information from other processed pixels.
  • Locality - Each pixel's position is determined based on the positions of a limited number of neighboring pixels.
  • 16/32-bit precision arithmetic – In general, a 16-bit integer data type is adequate for storage, and 32-bit floating-point arithmetic is sufficient for image processing.

Following are specific aspects essential for fast image processing.

  • Superior Image Processing Quality – Quality is critical in fast image processing. You can use various algorithms to accomplish the same image processing operation to get varying output quality and resource intensity. Resource-intensive algorithms using multilevel optimization can give you the necessary performance benefits and provide the output within a reasonable time compared to the fast but crude algorithms.
  • Maximum Performance – For maximizing “fast image processing” performance, you can either optimize software code or increase hardware resources such as the number of processors. When it comes to the price-to-performance ratio, a GPU outpaces a CPU, and you can reap its full potential using multilevel algorithm optimization and parallelization.
  • Reduced Latency – A GPU offers reduced latency as it takes less time to process an image due to its inherent parallel pixel processing architecture. On the other hand, a CPU provides modest latency as the parallelism is implemented at image lines, tiles, and frame level.

How GPU differs from CPU?

A range of differences makes GPUs superior to CPUs when it comes to fast image processing.

Cores 

While a CPU contains minute powerful cores, a GPU has hundreds of thousands of weak and smaller cores.

Number of Threads 

A CPU architecture allows each physical CPU core to execute two threads on two virtual cores such that an individual thread executes the instructions independently. On the other hand, a GPU uses single instruction, multiple threads (SIMT) architecture, where 32 (generally) threads work on the same instruction as against a single thread in a CPU.

Type of Processing 

Due to its architecture, a CPU is ideal for serial instruction processing, while a GPU is designed for parallel instruction processing.

Thread Implementation 

Using actual genuine thread rotation, a GPU launches instructions every time from different threads. With a parallel algorithm and high load, it proves to be more efficient as a hardware implementation and is ideal for implementing several image processing algorithms. Unlike a GPU, a CPU uses out-of-order execution.

Why is GPU Superior to CPU?

Speed 

Due to its parallel processing capability, a GPU is much faster than a CPU. For the hardware with the same production year, GPU peak performance can be ten-fold with significantly higher memory system bandwidth than a CPU. Further, GPUs provide superior processing power and memory bandwidth. They are up to 100 times faster than CPUs with non-optimized software without AVX2 instructions while performing tasks requiring large caches of data and multiple parallel computations.

Managing Load 

Unlike a CPU, a GPU can reduce memory subsystem load by dynamically changing the number of available registers (from 64 to 256 per thread).

Simultaneous Execution of Several Tasks 

Several hardware modules of GPU enable concurrent execution of entirely different tasks. For example, Asynchronous copy from and to GPU, image processing on Jetson, tensor kernels for neural networks, video decoding, and encoding, computations on GPU, DirectX, OpenGL, and Vulkan for rendering.

Shared Memory 

All modern GPUs come with shared memory, which is several times faster than the bandwidth of a CPU’s L1 cache. It’s designed explicitly for algorithms with a high degree of locality.

Embedded Applications 

GPUs offer significantly greater flexibility and a practical alternative for specialized embedded applications, including FPGAs (Field-Programmable Gate Arrays) and ASICs (Application-Specific Integrated Circuits).

Some Myths Related to GPUs

Overclocking can damage your card.

Overclocking may cause a reset of settings (mostly CPU), inconsistent behavior, or crash without any actual damage to the video card. Though heat and voltage can impact the card, modern GPUs are smart enough to either shut down or throttle to prevent damage.

Merely 96 kB of shared memory capacity for each multiprocessor.

If managed efficiently, 96 kB memory size is adequate for each multiprocessor.

Back and forth data copying to CPU can downgrade the performance.

It’s a myth. As the best solution, you can perform all processing on the GPU within a single task. You can copy the source data either once or asynchronously to the GPU and return the computation results to the CPU at the end.

Summary

To sum it up,

  • GPUs serve as an excellent solution for fast and complex image processing tasks and outperform CPUs significantly.
  • GPU’s parallel processing architecture results in processing time reduction for a single image.
  • High GPU performance software can offer high energy efficiency, lower hardware cost, and lower cost of ownership.
  • Further, GPU provides low power consumption, high performance, and flexibility for embedded and mobile applications and compete with highly specialized ASIC/FPGA solutions.

For more blogs on data science and cloud computing, checkout E2E Networks website. Also if you are interested in taking a GPU server trial feel free to reach out to me @ 7795560646.

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June 29, 2022

Project Management for AI-ML-DL Projects

Managing a project properly is one of the factors behind its completion and subsequent success. The same can be said for any artificial intelligence (AI)/machine learning (ML)/deep learning (DL) project. Moreover, efficient management in this segment holds even more prominence as it requires continuous testing before delivering the final product.

An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

  • Testing whether the solution matches the problem-

After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

  • Choosing the algorithm for the problem-

To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:

https://www.datacamp.com/blog/how-to-manage-ai-projects-effectively

https://appinventiv.com/blog/ai-project-management/#:~:text=There%20are%20six%20steps%20that,product%20on%20the%20right%20platform.

https://www.datascience-pm.com/manage-ai-projects/

https://community.pmi.org/blog-post/70065/how-can-i-manage-complex-ai-projects-#_=_

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
June 29, 2022

Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.

 

The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.

 

In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.

 

Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:

Wiom

Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 

TechVantage

As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online

Manthan

As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 

NetraDyne

Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.

 

Helpshift

Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 

Facilio

An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.

 

In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.

 

 

References

https://www.inventiva.co.in/trends/telecom-startup-funding-inr-30-crore/

https://www.mygreatlearning.com/blog/top-ai-startups-in-india/

This is a decorative image for Top 7 AI Startups in Education Industry
June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

https://belitsoft.com/custom-elearning-development/ai-in-education/ai-in-edtech

https://www.emergenresearch.com/blog/top-10-leading-companies-in-the-artificial-intelligence-in-education-sector-market

https://xenoss.io/blog/ai-edtech-startups

https://riiid.com/en/about

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