The tremendous advances in Integrated Circuit (IC) Design has brought us amazing products over the last decade. These products have permeated into our daily lives in more ways than we had ever imagined and now are an integral part of our day. The advances in IC's have also increased the complexity of Design Verification (DV) significantly. Design Verification, the process of verifying that an IC functions as intended, takes up more than 50% of the time and cost of designing an IC*.
Costs of DV is increasing, and, the time-to-market for new IC projects are slipping due to DV. To meet the growing demand for IC's we need to find innovative ways to speed up verification and reduce the associated costs. Additionally, as the research highlights, DV requires a significant amount of engineering talent and, the demand for DV Engineers grew at a 6.8% CAGR*.
Source: *2020 Wilson Research Group Functional Verification Study - Harry Foster, Siemens EDA
There are not enough DV engineers being produced to meet this demand
With the advent of Machine Learning techniques, there have been multiple instances of Machine Learning engineers doing better than Domain Experts on hard problems. For instance, Machine Engineers from Google Deep Mind (AlphaGO) beat the world GO champion Lee Sedol . This was an unprecedented achievement for ML Engineers, with little domain knowledge of the game GO.
Another stunning example of ML engineers doing better than Domain Experts is in the field of protein folding, where Google Deep Mind (Alpha Fold), a ML program predicted protein structures better than any domain expert.
Yet another example of ML engineers doing better than Domain Experts is in the area of Chip Floorplanning on an IC. A recently published paper in nature by Google researchers, demonstrates how a ML model did better than Domain experts.
It is rather unusual that all these achievements mentioned of ML engineers doing better than domain experts, come from one company, namely Google!
There are a growing number of examples of Domain Experts who are using Machine Learning to do better in their domains. For instance, researchers at Univ of Washington developed a ML model that does as good or better than Google Deep Mind (Alpha Fold).
Another example of Domain Experts doing better with ML: Researchers at the Center for Computational Imaging and Personalized Diagnostics at Case Western Reserve University have showed that the inclusion of hand-crafted features derived from deep understanding of the problem domain in conjunction with a ML model, significantly outperform more traditional approaches for lung cancer classification on CT scans and also for predicting cancer outcomes from digital pathology images.
Challenging fields such as Hardware and Software Verification will require domain experts to embrace Machine Learning techniques and for Machine Learning engineers to attack Verification problems from a vastly different perspective.
DV Engineer 2.0 is a metaphor for Domain Experts who use Machine Learning to do better in their domains, and vice-versa, where Machine Learning Engineers do better than domain experts in their domains.
Machine Learning and Software 2.0 will play an oversized role in helping the DV Engineer 2.0 to take on the challenge of reducing the cost and time of verification, while challenging the traditional Software 1.0 stack.