Artificial Intelligence-Enhanced, Multi-Level, Modular System Design

Technical Report · Sun Oct 01 04:00:00 EDT 2023

DOI:https://doi.org/10.2172/2430208· OSTI ID:2430208

Crowder, Douglas Cale ^[1]; Trappett, Matthew Lee ^[1]; Kuberry, Paul Allen ^[1]; Cardwell, Suma George ^[1]; Smith, John Darby ^[1]; Kumar, Suhas ^[2]; Chance, Frances S. ^[1]; Yi, Suin ^[3]; Swaminathan, Madhavan ^[4]; Sengupta, Abhronil ^[4]; Kumar, Rahul ^[4]; Manna, Bibhas ^[4]

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1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
3. Texas A & M Univ., College Station, TX (United States)
4. Pennsylvania State Univ., University Park, PA (United States)

As Moore’s Law and Dennard Scaling come to an end, it is becoming increasingly important to develop non-von Neumann computing architectures that can perform low-power computing in the domains of scientific computing, artificial intelligence, embedded systems, and edge computing. Next-generation computing technologies, such as neuromorphic computing and quantum computing, have the potential to revolutionize computing. However, in order to make progress in these fields, it is necessary to fundamentally change the current computing paradigm by codesigning systems across all system level, from materials to software. Because skilled labor is limited in the field of next-generation computing, we are developing artificial intelligence-enhanced tools to automate the codesign and co-discovery of next-generation computers. Here, we develop a method called Modular and Multi-level MAchine Learning (MAMMAL) which is able to perform analog codesign and co-discovery across multiple system levels, spanning devices to circuits. We prototype MAMMAL by using it to design simple passive analog low-pass filters. We also explore methods to incorporate uncertainty quantification into MAMMAL and to accelerate MAMMAL by using emerging technologies, such as crossbar arrays. Ultimately, we believe that MAMMAL will enable rapid progress in developing next-generation computers by automating the codesign and co-discovery of electronic systems.

• Research Organization: Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: NA0003525
• OSTI ID: 2430208
• Report Number(s): SAND--2023-10948
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING