Machine learning can reduce the time and complexity associated with circuit design.
The most commonly used machine learning algorithms used in EDA applications are supervised learning, unsupervised learning, active learning, and reinforced learning.
The fully automated routing and tuning of devices in electronic circuit designs are the best examples for the higher reliability, resilience, and performance obtained using machine learning algorithms.
Implementing machine learning to circuit design helps to make predictions about design complexity, errors, and delays without much human intervention.
Machine learning is an artificial intelligence technique that makes sensible predictions from a large set of inputs. Electronic design automation (EDA) is a field that deals with big data issues, and it takes a lot of time for humans to finalize these designs. Implementing machine learning to circuit design helps make predictions about design complexity, errors, and delays without requiring much human intervention. Let’s take a look at how machine learning-based circuit design has reduced time requirements and complexity in the circuit design process.
Electronic Circuit Design Challenges
Electronic circuit design is a time-consuming process. In circuit design, the use of computer-aided design (CAD) tools is common. However, the user must have the experience and expertise to use these CAD tools properly. The runtime of the software may be up to thousands of computing hours to design a circuit. The complete design cycle of a circuit may take up to one year. However, in most cases, errors are revealed in chips or boards after manufacturing and require re-spin. Time, as well as money, is wasted in the process of designing. Because of these reasons, designers need a more cost and time-efficient way to design, which is why machine learning is implemented in circuit design.
Machine Learning in Circuit Design
Machine learning reduces the time and complexity associated with designing analog circuits, digital circuits, mixed-signal circuits, and semiconductor chips. The main aim of applying machine learning in circuit design is to automate the design process, which reduces the design cycle time. Machine learning-based EDA accelerates the physical circuit design process without requiring a human. Machine learning algorithms shorten the design time in EDA, thus minimizing the design to market time.
How Does Machine Learning Aid the Circuit Design Process?
Machine learning employs algorithms to extract information from bundled data or prior knowledge. Machine learning collects data from finished designs and design processes in EDA tools. The machine learning-enabled EDA software learns from each design step and provides predictions on things like tools and design hints. Usually, machine learning-based circuit design collects the previous outputs of simulation and makes future predictions regarding routing patterns and placement of components.
Machine learning enables EDA software to give a tool parameter suggestion that is best suited for a given circuit design. With the help of powerful modeling and training, EDA tools attain the learning skills to provide the best recommendation for any given circuit design problem. Machine learning algorithms can help in identifying bottlenecks in circuit design and give predictions on how to proceed. When applied in EDA, machine learning employs several techniques and algorithms. We will discuss some of those algorithms in the upcoming section.
Machine Learning Algorithms for Circuit Design
The machine learning algorithms most commonly used in EDA applications are supervised learning, unsupervised learning, active learning, and reinforced learning.
Supervised learning: In supervised learning algorithms, the activity is executed by teaching the model using a labeled dataset. The labeled dataset can be numbers, characters, or categorical. Techniques such as classification or regression are employed in supervised learning algorithms.
Unsupervised learning: In an unsupervised learning algorithm, the model discovers the information on its own. The unsupervised learning algorithm uses unlabeled data and finds patterns and groups in it. Examples of techniques used in unsupervised learning algorithms include clustering, dimension reduction, and density estimation.
Active learning: In active learning-based machine learning models, samples are chosen from inputs and are refined during the searching process.
Reinforced learning: Machine learning models get rewards from actions executed in an environment. A model interacts with the environment through these actions. Reinforced learning aims to achieve maximum rewards and thus provide the best action.
The above-mentioned learning paradigms identify the relationship between the design specification and parameter output in a given circuit design problem. The application of machine learning in circuit design leads to faster convergence to parameters that satisfy the design specifications. By employing machine learning to EDA tools, an optimum design is achieved within minimum time. The fully automated routing and tuning of devices in electronic circuit designs are the best examples for the higher reliability, resilience, and performance obtained using machine learning algorithms.
It is a best practice to incorporate machine learning in circuit design to improve the performance, power, and area of the system under consideration. Cadence offers machine learning techniques that help provide better productivity, reduced time-to-market, and less manual intervention in the design process.
If you’re looking to learn more about how Cadence has the solution for you, talk to our team of experts.