Friday 28 March 2025
As technology continues to advance at a rapid pace, the need for efficient and accurate ways to design and optimize complex systems has become increasingly important. One such system is the integrated circuit (IC), which is the backbone of modern electronics. ICs are used in everything from smartphones to computers to medical devices, and their performance, power consumption, and area requirements have a direct impact on the overall functionality and cost of these devices.
To tackle this challenge, researchers have developed a new framework for predicting the performance, power consumption, and area (PPA) of ICs at an early stage in the design process. This approach uses machine learning techniques to analyze the RTL (register-transfer level) code that describes the behavior of the IC and predicts its PPA characteristics.
The key innovation behind this framework is the use of a simple operator graph (SOG) representation of the RTL code. Unlike traditional approaches that rely on complex abstract syntax trees, the SOG uses basic logic operations to model the circuit, making it easier to analyze and predict PPA characteristics. This approach also allows for more accurate predictions, as it takes into account the specific implementation details of the IC.
The framework has been tested on a range of benchmark circuits, with impressive results. For example, the predicted WNS (worst negative slack) values were found to be within 12% of the actual values, while the predicted TNS (total negative slack) values were within 24%. The power consumption predictions were also highly accurate, with an average error of just 33%.
One of the major advantages of this framework is its ability to generalize across different IC designs. This means that designers can use it to predict PPA characteristics for new, unseen circuits, without having to retrain the model or gather additional data. This could have a significant impact on the design process, allowing designers to make more informed decisions earlier in the development cycle.
The framework is not limited to predicting PPA characteristics alone; it also includes models for estimating timing violations and power consumption. These models are trained using data from a variety of sources, including RTL code, circuit simulations, and physical implementation details.
In addition to its accuracy and flexibility, this framework has several other benefits. For example, it can be used to identify potential design bottlenecks early in the development cycle, allowing designers to make targeted improvements. It also provides a more comprehensive understanding of IC behavior, which can help designers optimize their designs for better performance, power consumption, and area requirements.
Cite this article: “Predicting Performance, Power Consumption, and Area of Integrated Circuits Using Machine Learning Techniques”, The Science Archive, 2025.
Machine Learning, Integrated Circuits, Ic Design, Ppa Prediction, Rtl Code, Sog Representation, Abstract Syntax Trees, Circuit Simulations, Physical Implementation Details, Timing Violations, Power Consumption.
