A lot of AI design is done in software and, while much of it will remain there, increasing numbers of designs are finding their way into hardware. There are multiple reasons for this including the important goals of achieving lower power or higher performance for critical parts of the AI process. Imagine you need dramatically improved rate of object recognition in automated-driving applications.
Implementing an AI application in hardware presents some key challenges for the designer.
- Need to explore multiple algorithms and architectures, typically using a framework such as TensorFlow or Caffe
- Need to qualify power, performance, area, and accuracy trade-offs of various architectures
- Need a rapid path from the models to production silicon
In this article, I'll describe a flow that starts in the TensorFlow environment, moves into abstract C++ targeted at the Stratus HLS flow, and then into a concrete hardware implementation flow.
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