CORDIS Project
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This project develops an energy-efficient memory design framework for deep neural networks, focusing on optimizing performance for battery-powered devices. It incorporates advanced techniques like quantization and pruning to enhance energy-accuracy trade-offs in artificial intelligence applications.
Deep Neural Networks (DNNs) are the fundamental component in most artificial intelligence applications.
With the increasing number of applications based on artificial intelligence, the performance and energy efficiency of architectures running these algorithms have become crucial, especially for battery-powered platforms.
In this work, I propose an energy optimizing memory design framework with a special SRAM/in-memory-computing structure.
It also utilizes datapath optimization techniques like q…
BILKENT UNIVERSITESI VAKIF
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United States, Cambridge
Type: University / higher education
Activity type: Higher or Secondary Education Establishments
SME: No
France, Crolles
Type: Company (for-profit)
Activity type: Private for-profit entities (excluding Higher or Secondary Education Establishments)
SME: No
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