Abstract
The surge in scientific literature obscures breakthroughs and hinders the discovery of new research paths. We propose an artificial intelligence (AI) powered framework using large language models (LLMs) and knowledge graphs (KGs) to automate parts of scientific discovery, focusing on energy-efficient AI circuits. Our hybrid approach combines LLMs, structured data, and ontology-based reasoning to construct a comprehensive knowledge graph that integrates insights across computational neuroscience, spiking neuron models, learning rules, architectural motifs, and neuromorphic device technologies. This multi-domain representation enables the generation of hypotheses that connect biological function with implementable, energy-efficient hardware architectures. Using KG embeddings and graph neural networks, the framework generates hypotheses for novel circuits, validates them through optimization on exascale HPC systems, and with tools like SuperNeuro and Fugu, the most promising designs will be prototyped in hardware. This open-source system aims to accelerate discoveries and bridging neuroscience with hardware innovation, drive collaboration, and unlock new opportunities in low-power AI computing.
| Original language | English |
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| Title of host publication | GLSVLSI 2025 - Proceedings of the Great Lakes Symposium on VLSI 2025 |
| Publisher | Association for Computing Machinery |
| Pages | 996-1001 |
| Number of pages | 6 |
| ISBN (Electronic) | 9798400714962 |
| DOIs | |
| State | Published - Jun 29 2025 |
| Event | 35th Edition of the Great Lakes Symposium on VLSI 2025, GLSVLSI 2025 - New Orleans, United States Duration: Jun 30 2025 → Jul 2 2025 |
Publication series
| Name | Proceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI |
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Conference
| Conference | 35th Edition of the Great Lakes Symposium on VLSI 2025, GLSVLSI 2025 |
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| Country/Territory | United States |
| City | New Orleans |
| Period | 06/30/25 → 07/2/25 |
Funding
This manuscript has been authored in part by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. This article has been authored by an employee of National Technology & Engineering Solutions of Sandia, LLC under Contract No. DE-NA0003525 with the U.S. Department of Energy (DOE). The employee owns all right, title and interest in and to the article and is solely responsible for its contents. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).
Keywords
- Knowledge graphs
- STDP
- architectural synthesis
- cortical microcircuits.
- energy-efficient circuits
- hypothesis generation
- large language models
- neuromorphic computing
- scientific discovery automation
- spiking neural networks