[HTML][HTML] Overview of memristor-based neural network design and applications
L Ye, Z Gao, J Fu, W Ren, C Yang, J Wen, X Wan… - Frontiers in …, 2022 - frontiersin.org
Conventional von Newmann-based computers face severe challenges in the processing
and storage of the large quantities of data being generated in the current era of “big data.” …
and storage of the large quantities of data being generated in the current era of “big data.” …
A configurable multi-precision CNN computing framework based on single bit RRAM
Convolutional Neural Networks (CNNs) play a vital role in machine learning. Emerging
resistive random-access memories (RRAMs) and RRAM-based Processing-In-Memory …
resistive random-access memories (RRAMs) and RRAM-based Processing-In-Memory …
Structured pruning of RRAM crossbars for efficient in-memory computing acceleration of deep neural networks
The high computational complexity and a large number of parameters of deep neural
networks (DNNs) become the most intensive burden of deep learning hardware design …
networks (DNNs) become the most intensive burden of deep learning hardware design …
An ultra-efficient memristor-based DNN framework with structured weight pruning and quantization using ADMM
The high computation and memory storage of large deep neural networks (DNNs) models
pose intensive challenges to the conventional Von-Neumann architecture, incurring sub …
pose intensive challenges to the conventional Von-Neumann architecture, incurring sub …
Fault tolerance in neuromorphic computing systems
Resistive Random Access Memory (RRAM) and RRAM-based computing systems (RCS)
provide energy-efficient technology options for neuromorphic computing. However, the …
provide energy-efficient technology options for neuromorphic computing. However, the …
Cross-point resistive memory: Nonideal properties and solutions
Emerging computational resistive memory is promising to overcome the challenges of
scalability and energy efficiency that DRAM faces and also break through the memory wall …
scalability and energy efficiency that DRAM faces and also break through the memory wall …
Rescuing rram-based computing from static and dynamic faults
Emerging resistive random access memory (RRAM) has shown the great potential of in-
memory processing capability, and thus attracts considerable research interests in …
memory processing capability, and thus attracts considerable research interests in …
On minimizing analog variation errors to resolve the scalability issue of reram-based crossbar accelerators
Crossbar accelerators with a resistive random-access memory (ReRAM) are a promising
solution for accelerating neural network applications. The advantages of achieving high …
solution for accelerating neural network applications. The advantages of achieving high …
Design of fault-tolerant neuromorphic computing systems
Neuromorphic computing is rapidly becoming mainstream, and Resistive Random Access
Memory (RRAM) and RRAM-based computing systems (RCS) provide a promising …
Memory (RRAM) and RRAM-based computing systems (RCS) provide a promising …
Future computing platform design: A cross-layer design approach
Future computing platforms are facing a paradigm shift with the emerging resistive memory
technologies. First, they offer fast memory accesses and data persistence in a single large …
technologies. First, they offer fast memory accesses and data persistence in a single large …