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Synapse crossbar is an elementary structure in neuromorphic computing systems (NCS). However, the limited size of crossbars and heavy routing congestion impede the NCS implementation of large neural networks. In this paper, we propose a two-step framework (namely, group scissor) to scale NCS designs to large neural networks. The first step rank clipping integrates low-rank approximation into the training...
IBM TrueNorth chip uses digital spikes to perform neuromorphic computing and achieves ultrahigh execution parallelism and power efficiency. However, in TrueNorth chip, low quantization resolution of the synaptic weights and spikes significantly limits the inference (e.g., classification) accuracy of the deployed neural network model. Existing workaround, i.e., averaging the results over multiple copies...
As technology advances, artificial intelligence becomes pervasive in society and ubiquitous in our lives, which stimulates the desire for embedded-everywhere and human-centric intelligent computation paradigm. However, conventional instruction-based computer architecture was designed for algorithmic and exact calculations. It is not suitable for handling the applications of machine learning and neural...
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