Fully hardware-implemented memristor convolutional neural network
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Summary
The fabrication of high-yield, high-performance and uniform memristor crossbar arrays for the implementation of CNNs and an effective hybrid-training method to adapt to device imperfections and improve the overall system performance are proposed.
- Type
- article
- Published
- 2020-01-01
- Cited by
- 2,047
- References
- 43
- OpenAlex
- https://openalex.org/W3003821665
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:210948661
Keywords
Memristor, Neuromorphic engineering, Computer science, Memistor, Convolutional neural network
References
- Efficient and self-adaptive in-situ learning in multilayer memristor neural networks
- Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
- Training and operation of an integrated neuromorphic network based on metal-oxide memristors
- Experimental demonstration and tolerancing of a large-scale neural network (165,000 synapses), using phase-change memory as the synaptic weight element
- 1.1 Computing's energy problem (and what we can do about it)
- A 10-nW 12-bit accurate analog storage cell with 10-aA leakage
- Memory leads the way to better computing.
- What's next for WHO?
- Gradient-based learning applied to document recognition
- A 3.1 mW 8b 1.2 GS/s Single-Channel Asynchronous SAR ADC With Alternate Comparators for Enhanced Speed in 32 nm Digital SOI CMOS
- Deep learning with COTS HPC systems
- Deep Residual Learning for Image Recognition
- Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
- Demonstration of Convolution Kernel Operation on Resistive Cross-Point Array
- Improved Synaptic Behavior Under Identical Pulses Using AlOx/HfO2 Bilayer RRAM Array for Neuromorphic Systems
- Fully CMOS compatible 3D vertical RRAM with self-aligned self-selective cell enabling sub-5nm scaling
- Neuromorphic computing using non-volatile memory
- In-datacenter performance analysis of a tensor processing unit
- Face classification using electronic synapses
- Chaotic dynamics in nanoscale NbO2 Mott memristors for analogue computing
Cited by
- Accurate deep neural network inference using computational phase-change memory
- 4K-memristor analog-grade passive crossbar circuit
- Sneak, discharge, and leakage current issues in a high-dimensional 1T1M memristive crossbar
- Research progress on solutions to the sneak path issue in memristor crossbar arrays
- Challenges and Trends inDeveloping Nonvolatile Memory-Enabled Computing Chips for Intelligent Edge Devices
- Magnetic Properties of Electrospun Magnetic Nanofiber Mats after Stabilization and Carbonization
- Memory devices and applications for in-memory computing
- Implementation of Unbalanced Ternary Logic Gates with the Combination of Spintronic Memristor and CMOS
- Improved Uniformity of TaOx-Based Resistive Random Access Memory with Ultralow Operating Voltage by Electrodes Engineering
- An artificial synapse by superlattice-like phase-change material for low-power brain-inspired computing
- Fabrication of GaOx based crossbar array memristive devices and their resistive switching properties
- Neurohybrid Memristive CMOS-Integrated Systems for Biosensors and Neuroprosthetics
- In-memory computing to break the memory wall
- In-memory computing with ferroelectrics
- Enhancement of DC/AC resistive switching performance in AlOx memristor by two-technique bilayer approach
- Low-Complexity Vector Quantized Compressed Sensing via Deep Neural Networks
- The application of halide perovskites in memristors
- SSM: a high-performance scheme for in situ training of imprecise memristor neural networks
- Device and Circuit Architectures for In‐Memory Computing
- Pathways to efficient neuromorphic computing with non-volatile memory technologies
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