XC7K70T-3FB676I_AD1984JCPZ Introduction

The Alveo U55C card combines many of the key features required by today's HPC workloads to deliver increased data parallelism, superior memory card management, optimized data migration processing, and the best per-unit function in the Alveo portfolio. energy consumption.

Compared to the previous-generation dual-slot Alveo U280, the Alveo U55C offers superior computing density and doubles the HBM2 capacity to 16GB. The Alveo U55C card is a single-slot full-height half-length (FHHL) form factor with a maximum power consumption of only 150 watts.

XC7K70T-3FB676I_AD1984JCPZ

XC7K70T-2FBG676C

XC7K325T-2FGG900I XC7K325T-3FB900C XC7K325T-3FB900E XC7K325T-3FBG676C XC7K325T-3FBG676I.

XC7K325TFBG676 XC7K325TFBG900 XC7K325TFF676 XC7K325T-FFG676ABX XC7K325TFFG676CACX.

XC7K325T-3FBG900C XC7K325T-3FBG900I XC7K325T-3FF900C XC7K325T-3FF900E XC7K325T-3FF900I.

XC7K355T-2FF901C XC7K355T-2FF901I XC7K355T-2FFG676C XC7K355T-2FFG676I XC7K355T-2FFG900C.

XC7K70T-3FB676I_AD1984JCPZ

AD1984AJCPZ

XC7K355T-1FF901I XC7K355T-1FFG676C XC7K355T-1FFG676I XC7K355T-1FFG901 XC7K355T-1FFG901I.

XC7K325-2FFG676 XC7K325-2FFG900I XC7K325-2FG900I5CG XC7K325-2FGG900 XC7K325F-1FFG900I.

XC7K160T-3FBG676E XC7K160T-3FBG676I XC7K160T-3FF676C XC7K160T-3FF676I XC7K160T-3FFG676C.

XC7K32T-2FFG900I XC7K335T-2FF901I XC7K352T-FFG900ABX XC7K355T XC7K355T 2FF901I.

XC7K70T-3FB676I_AD1984JCPZ

Both Xilinx and EDA companies have decades of data and are now leveraging AI to make the most of it. However, an important challenge in adopting machine learning in EDA companies is the lack of more specialized technical accumulation in a specific field. In the past few years, Xilinx has invested heavily in the field of machine learning, continuously acquiring AI technology and talents.

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.

relevant information

_AD1984JCPZ Introduction

The Alveo U55C card combines many of the key features required by today's HPC workloads to deliver increased data parallelism, superior memory card management, optimized data migration processing, and the best per-unit function in the Alveo portfolio. energy consumption.

Compared to the previous-generation dual-slot Alveo U280, the Alveo U55C offers superior computing density and doubles the HBM2 capacity to 16GB. The Alveo U55C card is a single-slot full-height half-length (FHHL) form factor with a maximum power consumption of only 150 watts.

XC7K70T-3FB676I_AD1984JCPZ

XC7K70T-2FBG676C

XC7K325T-2FGG900I XC7K325T-3FB900C XC7K325T-3FB900E XC7K325T-3FBG676C XC7K325T-3FBG676I.

XC7K325TFBG676 XC7K325TFBG900 XC7K325TFF676 XC7K325T-FFG676ABX XC7K325TFFG676CACX.

XC7K325T-3FBG900C XC7K325T-3FBG900I XC7K325T-3FF900C XC7K325T-3FF900E XC7K325T-3FF900I.

XC7K355T-2FF901C XC7K355T-2FF901I XC7K355T-2FFG676C XC7K355T-2FFG676I XC7K355T-2FFG900C.

XC7K70T-3FB676I_AD1984JCPZ

AD1984AJCPZ

XC7K355T-1FF901I XC7K355T-1FFG676C XC7K355T-1FFG676I XC7K355T-1FFG901 XC7K355T-1FFG901I.

XC7K325-2FFG676 XC7K325-2FFG900I XC7K325-2FG900I5CG XC7K325-2FGG900 XC7K325F-1FFG900I.

XC7K160T-3FBG676E XC7K160T-3FBG676I XC7K160T-3FF676C XC7K160T-3FF676I XC7K160T-3FFG676C.

XC7K32T-2FFG900I XC7K335T-2FF901I XC7K352T-FFG900ABX XC7K355T XC7K355T 2FF901I.

XC7K70T-3FB676I_AD1984JCPZ

Both Xilinx and EDA companies have decades of data and are now leveraging AI to make the most of it. However, an important challenge in adopting machine learning in EDA companies is the lack of more specialized technical accumulation in a specific field. In the past few years, Xilinx has invested heavily in the field of machine learning, continuously acquiring AI technology and talents.

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.

relevant information