XC7K420T-2FFV1156I_AD1895AYRSZ Introduction

Despite the gradual slowdown of Moore's Law, the exponential growth in FPGA transistor counts over the past 20+ years has not diminished. EDA has long faced various challenges: the number of devices is increasing and the design is getting more complex.

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.

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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.

Xilinx's 2021 Automotive and Autonomous Driving Online Technology Conference will focus on topics such as lidar, millimeter-wave radar, and autonomous driving domain controllers.

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