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Implementation of AI and ML in VLSI design Technology

The newest buzzwords Artificial Intelligence, Machine Learning, and Deep learning as per the acuity of the common man are no longer confined to the IT or software domain, as the horizons have expanded. The trending branches of Engineering, like Computer Science Engineering and Electronics & Communication Engineering, are very closely related. Any major change in software technology necessitates improved computer hardware to support it.

To have a basic understanding of the terminology, machine learning denotes an AI system that can self-learn based on the algorithm. Whenever the Systems get smart over a period of time without human intrusion are Machine Learning and Deep Learning, i.e. machine learning applied to large data sets involving more layers for data analysis.

These terminologies are wound together, as most of the work related to Artificial Intelligence involves Machine Learning because smart behavior requires considerable information to absorb new methods. How the new techniques in machine learning are being implemented, the hardware systems will evolve rapidly. New architectures have come into existence including newer ways to process the data.

With the evolving times, Machine learning will require improved custom hardware to meet these developing designs. The domain and the scope of Machine learning are very vast. Many Inventors and developers are already in the market working towards upgradation. The  Systems and technologies are based on Algorithms, model Training, rules, etc where software and computer engineering play a prominent role. However, there is a need to understand that the Software requires very high-end hardware, has significant computation capacity, consumes less power, and is capable of performing complex mathematical functions with fractions of microseconds—–Electronics Behind.

How has technology evolved?

The advancements in chip technology, graphical processing units, sensors, communication networks, etc have come to the rescue and development of AI. Electronics and Communication Engineering play a very important part in these.

To overcome the difficulties at various development and design stages, the investigators presented artificial intelligence (AI) techniques in the growing domain of Chip design in VLSI and automation sector. At the initial stage the AI procedures like knowledge-based and skillful systems, attempt to state the problem and then select the fittest result from the field of various probable solutions. By incorporating and involving the latest design automation tools, there has been rapid and extraordinary development in the ever-growing VLSI technology and the upgradation from the design of VLSI chips to the Ultra Large Scale Integrated circuit systems. The integration of computer-aided design and program tools further improvised the computerization of VLSI design. The tools designed are capable of resolving diverse phases of the task design very proficiently. There are a few challenges that the system faces due to tool integration in one package i.e. the productivity and functionality of CAD programs reduce radically.

To tackle the issues of various multiple design stages, there was a felt need to amalgamate Artificial Intelligence techniques in VLSI design automation. Multiple solutions for the problem statement are being analyzed and the best possible is being chosen. It is a well-known fact that the ML models cannot be trained on Computer Processing Units due to their inefficiency in dealing with high-quality datasets (preferably images and videos), VLSI engineers design a Graphics Processing Unit to overcome the problem.

Similarly, all kinds of hardware that artificial intelligence requires can only be fabricated by the VLSI industry. It can be stated that the VLSI  domain and Artificial Intelligence are codependent.

Starting from the range of applications such as remote control, washing machine, cell phone, microwave oven, AC, Car electronics, Spaceships, aviation, weather forecast, satellites, and defence everywhere Electronics has penetrated. The digitization race has asked every day for new electronic systems having low power consumption, Higher battery backup, low cost, fastest computational speed, and very short design time.

As the size of components is shrinking day by day, the study which is responsible for designing all these electronics needs modernization at a faster pace. If VLSI engineers are not sparing day and night for this miniaturization the betterment of electronics and signaling systems will stop. The future will see a tremendous boost in the VLSI sector.

To enhance the apparent growth in the nanometre range in the integrated circuit industry it is necessary to introduce the methodologies to reduce the design complexity and to reduce the irregularities in the design while growing the chip. The most important and foremost agenda in design is to reduce the turnaround time of chip manufacturing. outdated methodologies applied for those employed for such responsibilities were majorly physical and not automated because of which the processing takes a longer amount of time and thus the process becomes very time-consuming and resource-intensive.

A comparison worth understanding

In comparison to old methodologies, the exclusive strategies of artificial intelligence (AI) offer several exhilarating methods for handling complex and data-concentrated tasks in the design and testing for VLSI. The complications and the delay in the process can be overcome by embedding and incorporating the latest techniques in the design of VLSI and Manufacturing. The processes which are incorporated use the automated learning algorithms of Artificial Intelligence and Machine Learning which helps in reducing the time and exertion for understanding and processing of the information.

The overall outcome enhances the Integrated circuit production while reducing the manufacturing turnaround time. The improvement in the overall turnaround time of a chip greatly depends upon the technology used in designing the system to overcome the overall design constraints. Electronic design automation can be utilized to produce an ideal solution for the set design constraints. Through this writeup, one gets to understand the application and need for an automated approach using the concepts of Artificial Intelligence and Machine Learning in VLSI design and manufacturing. Going through the series of applications, one finds scope in the future digitization by introducing various techniques to transform the field of VLSI design to design high-velocity, highly intelligent, and efficient implementations.

Taking the discussion further from Artificial Intelligence and Machine Learning to an understanding of one more concept of deep learning. Deep learning is a subsection of machine learning, which is in simple terms a neural network with more layers i.e. three or more. The role of these layers is to simulate the behavior of the human brain. It trains the system from huge data sets. A single-layered network can only make estimated calculations. The more the number of layers, the more the efficiency can be enhanced. These hidden layers help to optimize and improve the accuracy.

It is this deep learning technology that drives most of the artificial intelligence (AI) applications and services. This improves and optimizes the performance of any physical task without any human intervention. In any of the existing systems with multiple modern applications, the hidden technology is majorly deep learning technology. There are numerous day-to-day applications that use the concept of deep learning such as digital assistants, voice-enabled TV remotes, credit card fraud detection, self-driving cars, and many more.

Conclusion

With the enhancement in the performance in the semiconductor design, the geometry of the transistor is shrinking and the technology is upgrading from basic transistors to non-planar FinFET devices. Talking about the most common device used in Analog circuits is the operational Amplifiers. The design issues and size of the operational amplifier depend upon the need of the designer. It has been observed that the search process becomes time-consuming due to sizing issues. With the upcoming recent development of the Graphic processor unit which is based on deep learning technology, the size of the device can be automated and controlled.

Keeping in view the trends and growth of the chip technology there is higher need and demand.

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