Project description
Shifting from computation-based to logic-based approaches for deep neural networks
Artificial intelligence systems powered by deep neural networks (DNNs) face two major challenges: high energy consumption and limited transparency in decision-making. Current methods focus on speeding up computations rather than understanding how DNNs work internally. The ERC-funded LogiNet project will focus on the logic functions of DNNs rather than traditional computational methods. The project will embed pretrained weights directly into the circuits of MAC operations to reduce data movement and computational complexity. LogiNet explores the analysis of DNN properties such as hardware reliability and fairness in decision-making using high-level expressions. The proposed research paves the way for energy-efficient AI systems and enhances trust in their decision-making, benefiting applications from edge devices to large-scale data centres.
Objective
Deep neural networks (DNNs) have achieved great breakthroughs in many fields. However, DNNs require massive multiply-accumulate (MAC) operations and their execution on digital hardware causes formidable energy consumption. State-of-the-art solutions are still focusing on simply accelerating MAC operations in DNNs instead of examining their actual logic functions after training. The blind execution of massive MAC operations in inference, however, poses critical risks to performance and energy sustainability of AI systems. It also misses the opportunity of examining internal decision-making processes of DNNs for their verification in reliability-critical systems. To address these challenges, LogiNet explores a new perspective to study the execution of DNNs on hardware with their logic representations and high-level expressions, thus opening a new door for design methodologies to realize high-performance and low-energy DNN computing for a wide range of scenarios from edge devices to data centers. The high-level expressions of DNNs can also be used to analyze the properties of DNNs and thus contribute to the understanding of their actual functions. Specifically, LogiNet extracts logic representations of DNNs by embedding pretrained weights into the circuits of MAC operations to reduce data movement and computational complexity. This also constructs a bridge between MAC operations and their high-level expressions, which can be compiled and executed by CPUs and thus enable a flexible scheduling of DNN operations in modern heterogeneous computing systems. Furthermore, LogiNet explores the analysis of DNN properties such as hardware reliability and fairness in decision-making using high-level expressions. In short, LogiNet not only develops new solutions for green AI to benefit economy and environment but also lays the foundation for new directions such as logic-based DNN validation in the era of large-scale models that have started to influence our society fundamentally.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
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Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
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Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
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Call for proposal
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(opens in new window) ERC-2025-STG
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64289 Darmstadt
Germany
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