Implementing machine learning in digital circuit design, verification, synthesis, recognition, testing, and fault detection is of great practical importance due to the reduced requirements for human intervention.
Machine learning-based digital circuit design learns hard constraints such as desired electrical parameters and optimizes the design in terms of area, power, etc.
The design cycle implemented using machine learning algorithms applies different placement and routing patterns for different sub-circuits and takes care of noise coupling within the design.
Machine learning is emerging as an excellent way to deliver actionable insights across a wide range of applications in a variety of industries. The reasoning and intelligence that the machine learning domain exhibits resembles the human brain, ensuring that the decision-making process is as trustworthy as if a human were doing it. Machine learning provides sensible predictions from a large set of data.
Among the various applications that utilize machine learning algorithms, circuit design automation is gaining traction. Implementing machine learning in digital circuit design, verification, synthesis, recognition, testing, and fault detection is of great practical importance, as it reduces the need for human intervention. Optimized digital circuit development with fewer errors is encouraging the application of machine learning in digital circuit design. In this article, we will explore machine learning-based digital circuit activities.
Applying Machine Learning in Digital Circuit Design Automation
Machine learning is a promising tool to automate digital circuit design. Machine learning can replace the time-consuming, labor-intensive manual circuit design process with learning algorithms. Applying the knowledge gained from machine learning algorithms by examples has been proven to be feasible for digital circuit design.
How Does Machine Learning in Digital Circuit Design Work?
Machine learning learns to generate new circuit designs as per the circuit specifications and provides optimized circuits compared to manual designing. Machine learning-based digital circuit design learns hard constraints, such as desired electrical parameters, at first. Then, it learns to optimize the design in terms of area, power, etc.
In digital system design, the first step is to construct digital logic circuit diagrams using interconnects and basic logic gate symbols. Once a design is completed, the circuit diagram is incorporated in circuit simulators in the form of a schematic diagram or codes in hardware description languages to test the operation and verify the functionality of the circuit design.
Machine learning is a promising platform that can be used to automate digital circuit design verification, testing pattern generation, and fault detection. To automate all the above-mentioned design stages, the machine learning tools rely on models to verify the behavior of the circuit of interest. The drafting of the digital logic circuit and the conversion into a valid format can be automated using machine learning algorithms.
Learning by Example
Machine learning-based digital circuit designs by example are capable of generating design solutions under the following cases:
- When the input/output examples of the required circuit of interest are provided to the system.
- When additional examples are given for learning to refine the digital circuit design.
- When extra incompatible sets of examples are provided for learning.
The end result of machine learning by example-based digital circuit design is the development of a model describing the given examples and satisfying the given circuit specification.
We will discuss machine learning-based digital circuit testing and design verification in the upcoming section.
Machine Learning-Based Design Verification and Testing in Digital Circuits
Digital Design Verification
In digital design verification, for every bug detected, a complicated diagnosis is devised. However, there is redundancy with similar bugs, causing similar hardware problems and leading to analyzing one situation repeatedly. A machine learning-based automated hardware diagnosis can classify different bugs and localize the particular module affected by these bugs. With a machine learning-based verification process, the bugs are analyzed without a complex diagnosis.
In integrated circuit design, the rapid downscaling of integration increases the intricacies associated with the manufacturing process, especially that which involves deep submicron technology. Defects in nanoscale technology devices are becoming complex and unpredictable, making circuit testing and diagnosis difficult. The circuit testing solutions derived using machine learning tools help address the challenges in digital circuit manufacturing. The knowledge acquired from the data sets is used in machine learning to automate test generation and fault simulation and provide highly efficient test strategies.
Machine Learning in System-on-Chip (SoC) Design
The challenging task of layout design in SoC design can be simplified using a machine learning tool. The machine learning algorithm is trained for identifying the analog and digital sub-circuits in layout design automation.
In machine learning-based SoC layout design automation, device sorting is introduced to generate sequences for circuit components. In the next stage, machine learning algorithms are effectively applied for encoding circuit netlists. Through training, high accuracy is achieved in automatic component placement and routing integrated circuit design involving both analog and digital sub-circuits. The design cycle implemented using machine learning algorithms applies different placement and routing patterns for different sub-circuits and also takes care of noise coupling within the design.
The application of machine learning in digital circuit design reduces the need for human intervention and design time, thus bringing down the time-to-market. You can design digital circuits using Cadence’s PCB design and analysis tools.
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