SOURCE-LINKED INTELLIGENCE

Scalable, ferroelectric based accelerators for energy efficient edge AI

CORDIS · observation · Publication date unknown

Scalable, ferroelectric based accelerators for energy efficient edge AI The Ferro4EdgeAI project will provide an ultra-low power, scalable edge accelerator for artificial intelligence incorporating a memory augmented neural network, based on low cost, high density, multi-level, Back End of Line (BEoL) integrated ferroelectric (FE) technology. We expect to achieve a 2500x gain in energy-efficiency to break the POPS/W barrier with respect to the state-of-the-art CMOS accelerators and predictions for other emerging technology AI hardware. To do so, five ambitious specific objectives have been selected: - multi-level functionality in hafnia-based thin films by investigating the optimum trade-off in memory window, film thickness & stability of the ferroelectric state - low operating voltage for the non-volatile memory and robust multilevel operation of the FeFET-2 for high density logic operations and data storage. A low operating voltage is mandatory for power rating reduction, while robust multilevel operation is essential for analogue in-memory c

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recordType

award

status

SIGNED

region

EU

value

3750364.25

unit

EUR