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Fast Matrix Multiplication for AI

Project description

Recalculating AI matrix multiplication

AI tasks often involve processing large amounts of data, which are represented as matrices. Multiplying these matrices consumes both time and energy because the computer needs to perform numerous calculations to multiply each element of the matrices together. As the size of the matrices increases, so does the amount of computing power and energy required. Despite industry efforts to find a solution, the current cubic-time algorithms remain inefficient. In this context, the ERC-funded FMMF-AI project aims to revolutionise matrix multiplication. The project’s methods promise significant speedups in both software and hardware, thus addressing long-standing inefficiencies. It aims to seize promising business opportunities to make AI computation faster and more energy-efficient.

Objective

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.

Fields of science (EuroSciVoc)

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-ERC-POC - HORIZON ERC Proof of Concept Grants

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Call for proposal

Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.

(opens in new window) ERC-2022-POC2

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Host institution

THE HEBREW UNIVERSITY OF JERUSALEM
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 150 000,00
Address
EDMOND J SAFRA CAMPUS GIVAT RAM
91904 JERUSALEM
Israel

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Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

No data

Beneficiaries (1)

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