Abstract: Intelligent perceptual tasks such as vision and audition require the construction of good internal representations. Theoretical and empirical evidence suggest that the perceptual world is best represented by a multi-stage hierarchy in which features in successive stages are increasingly global, invariant, and abstract. An important challenge for Machine Learning is to devise "deep learning" methods than can automatically learn good feature hierarchies from labeled and unlabeled data. A class of such methods that combine unsupervised sparse coding, and supervised refinement will be described. We demonstrate the use of deep learning methods to train convolutional networks (ConvNets). ConvNets are biologically-inspired architectures consisting of multiple stages of filter banks, non-linear operations, and spatial pooling operations, analogous to the simple cells and complex cells in the mammalian visual cortex. A number of applications will be shown through videos and live demos, including a category-level object recognition system that can be trained on the fly, a pedestrian detector, and system that recognizes human activities in videos, and a trainable vision system for off-road mobile robot navigation. A new kind of "dataflow" computer architecture, dubbed NeuFlow, was designed to run these algorithms (and other vision and recognition algorithms) in real time on small, embeddable platforms. an FPGA implementation of NeuFlow running various vision applications will be demonstrated. An ASIC is being designed in collaboration with e-lab at Yale, which will be capable of 700 Giga-operations per second for less than 3 watts.