The current resurgence of artificial intelligence is due to advances in deep learning. Systems based on deep learning now exceed human capability in speech recognition [1], object classification [4], and playing games like Go [9]. Deep learning is enabled by powerful, efficient computing hardware [5]. The algorithms used have been around since the 1980s [7], but it has only been in the last few years - when powerful GPUs became available to train networks - that the technology has become practical. This paper discusses the circuit challenges in building deep-learning hardware both for inference and for training.
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