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Thesis
Home   /   Post Doctorat   /   Co-design strategy (SW/HW) to enable a structured spatio-temporal sparsity for NN inference/learning

Co-design strategy (SW/HW) to enable a structured spatio-temporal sparsity for NN inference/learning

Artificial intelligence & Data intelligence Computer science and software Engineering sciences Technological challenges

Abstract

The goal of the project is to identify, analyze and evaluate mechanisms for modulating the spatio-temporal sparsity of activation functions in order to minimize the computational load of transformer NN model (learning/inference). A combined approach with extreme quantization will also be considered.
The aim is to jointly refine an innovative strategy to assess the impacts and potential gains of these mechanisms on the model execution under hardware constraints. In particular, this co-design should also enable to qualify and to exploit a bidirectional feedback loop between a targeted neural network and a hardware instantiation to achieve the best tradeoff (compactness/latency).

Laboratory

Département Systèmes et Circuits Intégrés Numériques (LIST)
DSCIN
Laboratoire Intelligence Artificielle Embarquée
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