HOLIGRAIL

Holistic approaches to greener model architectures for inference and learning

Preview

Offer cutting-edge methods that significantly improve the energy efficiency and performance of artificial intelligence models

Olivier Sentieys, Professor at Université de Rennes, chair Inria

Olivier Bichler, Head of LIAE, CEA List

The HOLIGRAIL project look at a holistic, global comprehension of the energy consumption of AI algorithms, to provide breakthroughs in efficiency when running inference and training algorithms on specialized hardware.

The results are intended to be integrated into development solutions for embedded systems.

Key words : frugal AI, machine learning, artificial neural network, optimization, computer arithmetic, hardware architecture, hardware acceleration, optimization compiler, code theory

Missions

Our researches


Develop number representations

Design more compact and efficient number representations that maintain near-reference inference or learning quality, enabling better scalability of embedded models.


Develop training algorithms

Develop hardware-adapted training algorithms that improve sparsity, coding compactness and tensor transformations, in order to better exploit the constrained hardware resources in embedded systems.


Develop hardware solutions

Go beyond existing approaches by developing efficient hardware mechanisms optimized for sparsity, extreme quantization and ad hoc number representations; and by enhancing the interaction between hardware and algorithms for higher energy efficiency and performance.


Compiler optimization

Demonstrate the effectiveness of the compiler’s optimization methods, to ensure efficient implementation on embedded and high-performance platforms.

Consortium

Université de Rennes, CEA, Inria, INSA Lyon, Grenoble INP, CNRS

Consortium location

Autres projets

 SHARP
SHARP
Sharp theoretical and algorithmic principles for frugal ML
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 ADAPTING
ADAPTING
Adaptive architectures for embedded artificial intelligence
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 EMERGENCES
EMERGENCES
Near-physics emerging models for embedded AI
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 REDEEM
REDEEM
Resilient, decentralized and privacy-preserving machine learning
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 CAUSALI-T-AI
CAUSALI-T-AI
When causality and AI teams up to enhance interpretability and robustness of AI algorithms
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 FOUNDRY
FOUNDRY
The foundations of robustness and reliability in artificial intelligence
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 SAIF
SAIF
Safe AI through formal methods
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 PDE-AI
PDE-AI
Numerical analysis, optimal control and optimal transport for AI / "New architectures for machine learning".
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