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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...
The recently emerged research on “neuromorphic computing”, which stands for hardware acceleration of brain-inspired computing, has become one of the most active research areas in computer engineering. In this invited paper, we start with a background introduction of neuromorphic computing, followed by some examples of hardware acceleration schemes of learning and neural network algorithms on emerging...
The brain-inspired, spike-based neuromorphic system is highly anticipated in the artificial intelligence community due to its high computational efficiency. The recently developed memristor-crossbar-array technology, which is able to efficiently emulate the plasticity of biological synapses and accommodate matrix multiplication, has demonstrated its potential for neuromorphic computing. To facilitate...
Following technology scaling, on-chip heterogeneous architecture emerges as a promising solution to combat the power wall of microprocessors. This work presents <bold>Harmonica</bold>—aframework of heterogeneous computing system enhanced by memristor-based neuromorphic computing accelerators (NCAs). In Harmonica, a conventional pipeline is augmented with a NCA which is designed to speedup...
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...
The explosion of big data applications imposes severe challenges of data processing speed and scalability on traditional computer systems. However, the performance of the von Neumann machine is greatly hindered by the increasing performance gap between CPU and memory, motivating the active research on new or alternative computing architectures. As one important instance, neuromorphic computing systems...
As technology scales, on-chip heterogeneous architecture emerges as a promising solution to combat the power wall of microprocessors. In this work, we propose a heterogeneous computing system with memristor-based neuromorphic computing accelerators (NCAs). In the proposed system, NCA is designed to speed up the artificial neural network (ANN) executions in many high-performance applications by leveraging...
The invention of neuromorphic computing architecture is inspired by the working mechanism of human-brain. Memristor technology revitalized neuromorphic computing system design by efficiently executing the analog Matrix-Vector multiplication on the memristor-based crossbar (MBC) structure. However, programming the MBC to the target state can be very challenging due to the difficulty to real-time monitor...
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