However, the existing RRAM-based parallel computing architectures suffer from practical problems such as device variations and extra computing circuits. In this work, we propose a novel parallel computing architecture for pattern recognition by implementingk-nearest neighbor classification on metal-oxide ...
A RRAM-based Associative Memory Cell 作者: Pan Y.;Foster P.;Serb A.;Prodromakis T.;摘要: In general, intelligent systems require knowledge databases storing memory associations for mimicking the capabilities of the human brain. Conventional associative memory cells are constructed based on SRAM, ...
Finally, design consideration for the realization of functional NN hardware is summarized.doi:10.1016/B978-0-08-102782-0.00014-9Ying ZhouBin GaoChunmeng DouMeng-Fan ChangHuaqiang Wu
Resistive random-access memory (RRAM)-based in-memory computing (IMC) architectures offer an energy-efficient solution for DNN acceleration. However, the performance of RRAM-based IMC is limited by device nonidealities, ADC precision, and algorithm properties. To address this, in this work, first...
36 However, there are also some challenges in RRAM-based. CIM system design. Realizing such a system with high computing density, energy efficiency, computing accuracy and on-chip learning capability is a co-design task involving the design of algorithm, architecture, circuit, array, and device....
The parallel updating scheme of RRAM-based analog neuromorphic systems based on sign stochastic gradient descent (SGD) can dramatically accelerate the training of neural networks. However, sign SGD can decrease accuracy. Also, some non-ideal factors of RRAM devices, such as intrinsic variations and ...
Switched to using the Zephyr Memory Storage (ZMS) instead of Non-Volatile Storage (NVS) for all devices that uses the RRAM non-volatile memory (currently only the nRF54L15). Jira: NCSDK-29634
Resistive switching memory (RRAM) is considered as one of the most promising devices for parallel computing solutions that may overcome the von Neumann bottleneck of today's electronic systems. However, the existing RRAM-based parallel computing architectures suffer from practical problems such as device...
This branch is up to date with thuime/XPEsim:master.Folders and files Latest commit Cannot retrieve latest commit at this time. History4 Commits doc sim_examples simulator .gitignore LICENSE README.md Repository files navigation README License XPEsim A simulator for RRAM-based neural ...
中文题名:LayCO:Achieving Least Lossy Accuracy for Most EfficientRRAM-Based Deep Neural Network Accelerator via Layer-Centric Co-Optimization 英文题名:LayCO: Achieving Least Lossy Accuracy for Most Efficient RRAM-Based Deep Neural Network Accelerator via Layer-Centric Co-Optimization 作者:Zhao, Shao-Feng...