Machine Learning… Everywhere | Machine Learning - SemiEngineering
How ML can enable self-optimizing tools that look for DRC hotspots, EM/IR distribution, and more, notes Stelios Diamantidis, director of AI products and research at Synopsys.
AI is transforming the world around us, creating an avenue to
innovation across all sectors of the global economy. Today, AI can
interact with humans through natural language; identify bank fraud and
protect computer networks; drive cars around city streets; and play
complex games like chess and Go. Machine-learning is offering solutions
to many complex problems around us where analytical solutions may be too
expensive or practically impossible. How about chip design? Can ML
offer solutions to key problems in semiconductor engineering?
A deluge of design challenges
Over the years, the EDA industry has offered many solutions in the
modeling and design creation of complex systems. Most design problems in
EDA are NP-hard; there are simply no polynomial-time algorithms to
solve these problems and hence an optimal solution cannot be identified
analytically. Today’s EDA systems are finding it difficult to keep up
with advanced process node requirements due to a deluge of new design
challenges (figure 1).
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| Figure 1 |
To make things worse, these requirements are interdependent and need to
be considered concurrently across multiple planes of design
optimization. The actual application and techniques used depend highly
on each specific problem space. How does one prepare a general solution
for a specific problem when there is limited access to the design
environment?
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Source: SemiEngineering
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