This white paper explores the architecture, applications, and benefits of using
Xilinx's new AI Engine for compute intensive applications like 5G cellular and
machine learning DNN/CNN.
5G requires between five to 10 times higher compute density when compared
with prior generations; AI Engines have been optimized for DSP, meeting both
the throughput and compute requirements to deliver the high bandwidth and
accelerated speed required for wireless connectivity.
The emergence of machine learning in many products, often as DNN/CNN
networks, dramatically increases the compute-density requirements.
AI Engines, which are optimized for linear algebra, provide the computedensity
to meet these demands—while also reducing the power consumption
by as much as 50% when compared to similar functions being performed in
programmable logic.
AI Engines are programmed using a C/C++ paradigm familiar to many
programmers. AI Engines are integrated with Xilinx's Adaptable And Scalar
Engines to provide a highly flexibly and capable overall solution.