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In this work, we propose a regularized learning method that is able to take into account the deviation of the memristor-mapped synaptic weights from the target values determined during the training process. Experimental results obtained when utilizing the MNIST data set show that compared to the conventional learning method which considers the learning and mapping processes separately, our learning...
As a large-scale commercial spiking-based neuromorphic computing platform, IBM TrueNorth processor received tremendous attentions in society. However, one of the known issues in TrueNorth design is the limited precision of synaptic weights. The current workaround is running multiple neural network copies in which the average value of each synaptic weight is close to that in the original network. We...
Brain inspired neuromorphic computing has demonstrated remarkable advantages over traditional von Neumann architecture for its high energy efficiency and parallel data processing. However, the limited resolution of synaptic weights degrades system accuracy and thus impedes the use of neuromorphic systems. In this work, we propose three orthogonal methods to learn synapses with one-level precision,...
As the fourth basic circuit element, memristor has a unique synapse-alike feature which demonstrates great potentials in neuromorphic circuit design. However, a large gap exists between the theoretical memristor characteristics and the actual device behavior. For example, though the continuous changing in resistance state is expected in neuromorphic circuit design, it is difficult to maintain arbitrary...
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...
Neuromorphic computing is used for accelerating the computation of neural network which can simulate the brain of animal and composed by neurons and synapses. However, the neuromorphic computing with the traditional computer architecture leads to serious von Neumann bottleneck because of the gap between high frequency CPU computation and memory access. The emerging memristor is an innovation technology...
The rapid growth of computing capacity of modern microprocessors enables the wide adoption of machine learning and neural network models. The ever-increasing demand for performance, combining with the concern on power budget, motivated the recent research on hardware acceleration for these learning algorithms. A wide spectrum of hardware platforms have been extensively studied, from conventional heterogeneous...
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