Field-Programmable Dynamic Deep Learning

This paper introduces Neural Connect, a novel approach for the run-time adaptation of dynamic Deep Neural Networks (dynamic DNNs) at both the high, application level, and the low-level of individual processing elements within each layer. At the application level we make neural network layers run-time configurable, supporting both mapping operationslike activation functions-and reduction operations-such as multiply-accumulate and convolution. Within layers we allow efficient, scalable sharing of low-level processing and memory resources among any number of neurons, employing compute pools of heterogeneous processing elements to carry out layer operations. We separate high-level application development from its low-level deployment, enabling neural networks and their processing elements to be developed independently.