Machine learning (ML) has already proved its value in performing tough computational tasks that drive a wide variety of artificial intelligence applications. From facial recognition, real-time voice and video stream analysis, Big Data analytics to industrial machinery sensors, ML is becoming increasingly powerful and pervasive. As ML moves out of the cloud and the data center and into a growing variety of edge devices and applications—from self-driving automobiles and internet of things (IoT) environments to mobile devices and automated services, designers of ML-enabled devices face critical decisions regarding the right hardware and software to use.
This must-read guide explores key considerations when choosing the right processor IP mix for an ML application to ensure an optimal balance of ML system performance, cost and product design. It also offers advice on how to approach these critical decisions.
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