In the dynamic world of VLSI (Very Large-Scale Integration), the demand for innovative products is higher than ever. The journey from a concept to a fully functional product involves many challenges and uncertainties where design verification plays a critical role in ensuring the functionality and reliability of complex electronic systems by confirming that the design meets its intended requirements and specifications.

The rapid growth of artificial intelligence and machine learning has led to the development of numerous deep learning frameworks. Each framework has its strengths and weaknesses, making it challenging to deploy models across different platforms. However, the Open Neural Network Exchange (ONNX) framework has emerged as a powerful solution to this problem. This article introduces the ONNX framework, explains its basics, and highlights the benefits of using it.

Research and Markets estimates that the global VLSI market will be worth USD 662.2 billion in 2023. Market analysts predict that it will be worth USD 971.71 billion in 2028, growing at an 8% Compound Annual Growth Rate (CAGR). The development of VLSI technology has opened up new possibilities in the field of microelectronics. The landscape of electronic systems has been fundamentally changed by VLSI technology, which can combine millions of transistors onto a single chip. This ground-breaking innovation has produced extremely advanced and effective electronic devices that are incredibly powerful and small.

The Global CICD tool market is expected to see significant compound annual growth rate (CAGR) of 57.38% from 2023 to 2029, according to the report published by market intelligence data research. Nowadays, Containers has become a leading CICD deployment technique. By employing appropriate connections with Source Code Management systems (SCM) like GIT, Jenkins is able to start a build procedure each time a developer contributes his code.

Embedded systems have become increasingly important in today's world of automation. Particularly in the field of industrial automation, embedded systems play an important role in speeding up production and controlling factory systems.

Embedded platforms have become an integral part of our daily lives, revolutionizing our technological interaction. These platforms, equipped with deep learning algorithms, have opened a world of possibilities, enabling smart devices, autonomous systems, and intelligent applications. The deployment of deep learning algorithms on embedded platforms is crucial.

A Machine Learning (ML) pipeline is used to assist in the automation of machine learning processes. They work by allowing a sequence of data to be transformed and correlated in a model that can be tested and evaluated to achieve a positive or negative outcome.