10/30/2023 0 Comments Synopsys austin![]() ![]() ![]() New computer architectures, powered by low precision arithmetic engines (FP16 for training and INT8 for Inference), have laid the foundation for high performance AI systems - however, there remains an insatiable desire for AI compute with much higher power-efficiency and performance. Over the past decade, Deep Neural Network (DNN) workloads have dramatically increased the computational requirements of AI Training and Inference systems - significantly outpacing the performance gains obtained traditionally using Moore's law of silicon scaling. The precision scaling powered performance roadmap for AI Inference and Training systems
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