Computation reuse in DNNs by exploiting input similarity
In recent years, Deep Neural Networks (DNNs) have achieved tremendous success for
diverse problems such as classification and decision making. Efficient support for DNNs on
CPUs, GPUs and accelerators has become a prolific area of research, resulting in a plethora
of techniques for energy-efficient DNN inference. However, previous proposals focus on a
single execution of a DNN. Popular applications, such as speech recognition or video
classification, require multiple back-to-back executions of a DNN to process a sequence of …
diverse problems such as classification and decision making. Efficient support for DNNs on
CPUs, GPUs and accelerators has become a prolific area of research, resulting in a plethora
of techniques for energy-efficient DNN inference. However, previous proposals focus on a
single execution of a DNN. Popular applications, such as speech recognition or video
classification, require multiple back-to-back executions of a DNN to process a sequence of …