Hardware-Aware Machine Learning: Modeling and Optimization

Understand

Recent breakthroughs in Deep Learning (DL) applications have made DL models a key component in almost every modern computing system.

• The increased popularity of DL applications deployed on a wide-spectrum of platforms have resulted in a plethora of design challenges related to the constraints introduced by the hardware itself.
• What is the latency or energy cost for an inference made by a Deep Neural Network (DNN)? Is it possible to predict this latency or energy consumption before a model is trained? If yes, how can machine learners take advantage of these models to design the hardware-optimal DNN for deployment? From lengthening battery life of mobile devices to reducing the runtime requirements of DL models executing in the cloud, the answers to these questions have drawn significant attention.
• One cannot optimize what isn't properly modeled.