XC7K480T-1FF901I_AD1941XSTZ 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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The Vitis platform supports mainstream AI frameworks such as PyTorch and TensorFlow, as well as high-level programming languages such as C, C++, and Python, enabling developers to create domain solutions using specific APIs and function libraries, or use Xilinx software development kits in data centers. Accelerate critical HPC operational loads with ease.

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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