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SYCLOPS

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Scaling extreme analYtics with Cross architecture acceLeration based on OPen Sta...

Scaling extreme analYtics with Cross architecture acceLeration based on OPen Standards The wide-spread adoption of AI and analytics has resulted in a rapidly expanding market for novel hardware accelerators that can provide energy-efficient scaling of training and inference tasks at both the cloud and edge. Unfortun... ver más

31/12/2025

Líder desconocido

4M€

Presupuesto del proyecto: 4M€

Líder del proyecto Líder desconocido

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-11-17

HORIZON-CL4-2022-DATA-01-05: Extreme data mining, aggregation an... ExpectedOutcome:Proposal results are expected to contribute to the following expected outcomes:

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Duración del proyecto: 37 meses Fecha Inicio: 2022-11-17
Fecha Fin: 2025-12-31

Líder del proyecto Líder desconocido

Presupuesto del proyecto 4M€

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Descripción del proyecto The wide-spread adoption of AI and analytics has resulted in a rapidly expanding market for novel hardware accelerators that can provide energy-efficient scaling of training and inference tasks at both the cloud and edge. Unfortunately, all popular solutions AI acceleration solutions today use proprietary, closed hardware—software stacks, leading to a monopolization of the AI acceleration market by a few large industry players. The vision of SYCLOPS project is to enable better solutions for AI/data mining for extremely large and diverse data by democratizing AI acceleration using open standards, and enabling a healthy, competitive, innovation-driven ecosystem for Europe and beyond. This vision relies on the convergence of two important trends in the industry: (i) the standardization and adoption of RISC-V, a free, open Instruction Set Architecture (ISA), for AI and analytics acceleration, and (ii) the emergence and growth of SYCL as a cross-vendor, cross-architecture, data parallel programming model for all types of accelerators, including RISC-V. The goal of project SYCLOPS is to bring together these standards for the first time in order to (i) demonstrate ground-breaking advances in performance and scalability of extreme data analytics using a standards-based, fully-open, AI acceleration approach and (ii) enable the development of inter-operable (open and vendor neutral interfaces/APIs), trustworthy (verifiable and standards-based hardware/software), and green (via application-specific processor customization) AI systems. In doing so, we will use the experience gained in SYCLOPS to contribute back to SYCL and RISC-V standards and foster links to respective academic, industrial and innovator communities (RISC-V foundation, EPI, Khronos, ISO C++). Bringing together the two standards enables codesign in both standards, which in turn, will enable a broader AI accelerator design space, and a richer ecosystem of solutions.

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