XC7K410T-2FF676C_AD22100AR Introduction

Leveraging RoCE v2 and data center connectivity combined with 200 Gbps of bandwidth, this API-driven clustering solution enables Alveo networks to rival InfiniBand networks in performance and latency, without the need for vendor-specific restrictions. .

Not only does machine learning help improve QoR, it also reduces compile times and predicts and accelerates design closure strategies based on design patterns. Studies have shown a 10% improvement in QoR over the original compared to traditional EDA algorithms.

XC7K410T-2FF676C_AD22100AR

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XC7K410T-2FF676C_AD22100AR

Architecturally, FPGA accelerators like Alveo cards provide the highest performance at the lowest cost for many compute-intensive workloads. We enable customers to build Alveo HPC clusters on existing infrastructure and networks by introducing a standards-based approach.

It is the industry's first FPGA EDA tool suite based on machine learning optimization algorithms and an advanced, team-oriented design flow. It improves QoR by an average of 10% with machine learning-based algorithms, and reduces compilation time with modular design. On average, it was shortened by a factor of 5. In June of this year, Xilinx released Vivado ML Edition.

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