91³Ô¹ÏÍø

ML and Power, Performance, Area Challenges

There are several obvious areas in EDA that can benefit from ML, including modeling, parasitic extraction, routing and yield analysis. However, one area of increasing challenge for all designers is meeting the all-important power, performance, and area (PPA) targets ¨C an ideal candidate for application of ML-based optimization. As designs continue to increase in complexity due to more features, advanced processes, variation, and more operating scenarios, it is causing the engineering change order (ECO) flows for design closure to become more data intensive and time consuming.  This is becoming especially challenging as process technologies advance to 5nm and below.

Practical ML Approaches and Tools for ECO Optimization

Machine learning comes across as doctor-ordered formula for this symphony of complexities surrounding ECO power optimization. The most common ML method comprises the following steps: build a data bank, train the algorithm, create a model, and predict outcome for new input data. In the case of power optimization, it means learning from the ECO observed data and making fast and accurate predictions on power recovery choices without costly computations, for example, picking the best replacement cell for downsizing from, say 200 candidate library cells with different timing, power, and other complex characteristics.

Though collecting large amounts of data across design types and process nodes sounds attractive to improve model outcome, it is not an easy task and may not be required to achieve the desired quality of results (QoR). Most design decisions are relevant only in the context of their spatial or temporal proximity with-respect-to design architecture and versions; so, training data based on uncorrelated design points may not improve QoR. An alternate and practical ML method is ¡°Active Learning,¡± which interacts with the optimization engine on-the-fly to build relevant learning models based on local design data. This significantly simplifies the optimization path to achieve signoff PPA with faster turnaround time and less resource overhead, providing a strong incentive for adoption.

Synopsys PrimeTime suite is widely recognized as a standard for timing and power ECO and signoff. Its broad usage experience across an extensive range of application designs and process nodes enables it to more effectively address growing PPA challenges in design closure while offering advanced productivity and resource-efficiency technologies including machine learning.

Machine learning excitement is indeed justified. It clearly has potential to bring significant value to EDA and design, especially for time-consuming ECO optimization steps to improve productivity and achieve targeted PPA. An active ML approach provides an effective practical method to enable designers to incorporate the technology easily into their design flows and regain their power in a smarter way.