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The cross-point array architecture with resistive synaptic devices has been proposed for on-chip implementation of weighted sum and weight update in the training process of learning algorithms. However, the non-ideal properties of the synaptic devices available today, such as the nonlinearity in weight update, limited ON/OFF range and device variations, can(More)
This work presents the optimized design of a physical unclonable function (PUF) primitive based on the cross-point resistive random access memory (RRAM) array. The randomness of the PUF comes from the resistance variation of RRAM cells in the array. A four-cell selection scheme is proposed to create a large number of challenge-response pairs necessary for(More)
Technology-design co-optimization methodologies of the resistive cross-point array are proposed for implementing the machine learning algorithms on a chip. A novel read and write scheme is designed to accelerate the training process, which realizes fully parallel operations of the weighted sum and the weight update. Furthermore, technology and design(More)
Resistive Random Access Memory (ReRAM) has several advantages over current NAND Flash technology, highlighting orders of magnitude lower access latency and higher endurance. Recently proposed 3D vertical cross-point ReRAM (3D-VRAM) architecture is an encouraging development in ReRAM's evolution as a cost-competitive solution, and thus attracts a lot of(More)
Memristor-based neuromorphic computing system provides a promising solution to significantly boost the power efficiency of computing system. Memristor-based neuromorphic computing system has a wide range of design choices, such as the various memristor crossbar cell designs and different parallelism degrees of peripheral circuits. However, a memristor-based(More)
Analog conductance of resistive memories is attractive for implementing the weights in neuro-inspired algorithms. One of the most popular deep learning algorithms is the convolutional neural network (CNN). In this paper, we review our recent progress on using resistive memories for neuro-inspired computing. First, we optimized the iterative programming(More)
A neuro-inspired computing paradigm beyond the von Neumann architecture is emerging and it generally takes advantage of massive parallelism and is aimed at complex tasks that involve intelligence and learning. The cross-point array architecture with synaptic devices has been proposed for on-chip implementation of the weighted sum and weight update in the(More)