FMMF-AI
Financiado
Fast Matrix Multiplication for AI
Matrix multiplication consumes huge amount of resources: computing time and energy, primarily in AI applications. The industry has recognized the need for faster and more energy-efficient matrix multiplication with state-of-the-ar...
Matrix multiplication consumes huge amount of resources: computing time and energy, primarily in AI applications. The industry has recognized the need for faster and more energy-efficient matrix multiplication with state-of-the-art solutions in software (e.g., DGEMM of Intel's math kernel library (MKL) for CPU and NVIDIA's CUDA for GPU) and hardware (e.g., Google's TPU and Intel / Habana labs Gaudi accelerator). Unfortunately, all present solutions employ a wasteful cubic-time algorithm. We have developed methods that provide speedup for matrix multiplication in SW and in HW. The novel developments of Prof. Oded Schwartz and his strong team are based on years of research, and are protected by several patents. The funds are requested to pursue business opportunity.
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Duración del proyecto: 18 meses
Fecha Inicio: 2023-03-23
Fecha Fin: 2024-09-30
Fecha Fin: 2024-09-30
Línea de financiación: concedida
El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2024-09-30
Línea de financiación objetivo
El proyecto se financió a través de la siguiente ayuda:
ERC-2022-POC2:
ERC PROOF OF CONCEPT GRANTS2
Cerrada
hace 2 años
Presupuesto
El presupuesto total del proyecto asciende a
150K€
Líder del proyecto
THE HEBREW UNIVERSITY OF JERUSALEM
No se ha especificado una descripción o un objeto social para esta compañía.
Perfil tecnológico
TRL
4-5
Perfil tecnológico
TRL
4-5