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Cognitive computing - which learns to do useful computational tasks from data, rather than by being programmed explicitly - represents a fundamentally new form of computing. Unfortunately, Deep Neural Networks (DNNs) learn from repeated exposure to huge datasets, which currently requires extensive computation capabilities (such as many GPUs) working together over days or weeks of time. To accelerate...
Machine Learning (ML) is an attractive application of Non-Volatile Memory (NVM) arrays [1,2]. However, achieving speedup over GPUs will require minimal neuron circuit sharing and thus highly area-efficient peripheral circuitry, so that ML reads and writes are massively parallel and time-multiplexing is minimized [2]. This means that neuron hardware offering full ‘software-equivalent’ functionality...
We assess the impact of the conductance response of Non-Volatile Memory (NVM) devices employed as the synaptic weight element for on-chip acceleration of the training of large-scale artificial neural networks (ANN). We briefly review our previous work towards achieving competitive performance (classification accuracies) for such ANN with both Phase-Change Memory (PCM) [1], [2] and non-filamentary...
The total power budget of Ultra-Low Power (ULP) VLSI Systems-on-Chip (SoCs) is often dominated by the leakage power of embedded memories as well as status registers. On the one hand, supply voltage scaling down to the near-threshold (near- ) or even to the subthreshold (sub- ) domain is a commonly used, efficient technique to reduce both leakage power and active energy dissipation...
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