As neural network techniques are applied to consumer applications, designers must figure out how to introduce these computationally demanding algorithms while minimizing power consumption. This presentation will discuss how to balance the tradeoffs between performance, power, area, and bandwidth in AI applications. It will cover the evolution of CNN graphs, and describe the attributes of popular graphs such as Masknet, ICNET, and RetinaNet for IoT and smart home applications. Finally, it will touch on how an embedded vision processor architecture can maximize computational efficiency without sacrificing accuracy, using facial recognition for the IoT as an example.
Bruno Lavigueur, ASIC Digital Design Engineer, Synopsys
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