Efficient Computer Arithmetic for Deep Learning Computation


Seok-Bum Ko

University of Saskatchewan

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Deep learning can provide superior performance in many fields of applications. However, the cost of implementing deep learning models in practical applications is expensive. Deep learning models are both computation intensive and memory intensive. Computation is an important aspect for deep learning. It can determine the latency that is how fast the results can be obtained. In this seminar, computer arithmetic for deep learning will be discussed. This lecture will start with discussing the computation requirements of deep learning models and layers. Then, several computer arithmetic designs for deep learning in the literature will be used as examples. Finally, future trends of computer arithmetic for deep learning computation will be discussed.