Hardware implementation of memristor-based artificial neural networks

F Aguirre, A Sebastian, M Le Gallo, W Song… - Nature …, 2024 - nature.com
Artificial Intelligence (AI) is currently experiencing a bloom driven by deep learning (DL)
techniques, which rely on networks of connected simple computing units operating in …

[HTML][HTML] Modeling and simulating in-memory memristive deep learning systems: An overview of current efforts

C Lammie, W **ang, MR Azghadi - Array, 2022 - Elsevier
Deep Learning (DL) systems have demonstrated unparalleled performance in many
challenging engineering applications. As the complexity of these systems inevitably …

Characterizing and modeling non-volatile memory systems

Z Wang, X Liu, J Yang, T Michailidis… - 2020 53rd Annual …, 2020 - ieeexplore.ieee.org
Scalable server-grade non-volatile RAM (NVRAM) DIMMs became commercially available
with the release of Intel's Optane DIMM. Recent studies on Optane DIMM systems unveil …

A survey of neuromorphic computing-in-memory: Architectures, simulators, and security

F Staudigl, F Merchant, R Leupers - IEEE Design & Test, 2021 - ieeexplore.ieee.org
This work is a survey of neuromorphic computing-in-memory. Unlike existing surveys that
focus on hardware or application-level perspectives, the authors elaborate on architectures …

GraphA: An efficient ReRAM-based architecture to accelerate large scale graph processing

SA Ghasemi, B Jahannia, H Farbeh - Journal of Systems Architecture, 2022 - Elsevier
Graph analytics is the basis for many modern applications, eg, machine learning and
streaming data problems. With an unprecedented increase in data size of many emerging …

NEUTRAMS: Neural network transformation and co-design under neuromorphic hardware constraints

Y Ji, YH Zhang, SC Li, P Chi, CH Jiang… - 2016 49th Annual …, 2016 - ieeexplore.ieee.org
With the recent reincarnations of neuromorphic computing comes the promise of a new
computing paradigm, with a focus on the design and fabrication of neuromorphic chips. A …

Extreme heterogeneity 2018-productive computational science in the era of extreme heterogeneity: Report for DOE ASCR workshop on extreme heterogeneity

JS Vetter, R Brightwell, M Gokhale, P McCormick… - 2018 - osti.gov
The 2018 Basic Research Needs Workshop on Extreme Heterogeneity identified five Priority
Research Directions for realizing the capabilities needed to address the challenges posed …

Supermem: Enabling application-transparent secure persistent memory with low overheads

P Zuo, Y Hua, Y **e - Proceedings of the 52nd Annual IEEE/ACM …, 2019 - dl.acm.org
Non-volatile memory (NVM) suffers from security vulnerability to physical access based
attacks due to non-volatility. To ensure data security in NVM, counter mode encryption is …

AccelTran: A sparsity-aware accelerator for dynamic inference with transformers

S Tuli, NK Jha - IEEE Transactions on Computer-Aided Design …, 2023 - ieeexplore.ieee.org
Self-attention-based transformer models have achieved tremendous success in the domain
of natural language processing. Despite their efficacy, accelerating the transformer is …

Hardware/software cooperative caching for hybrid DRAM/NVM memory architectures

H Liu, Y Chen, X Liao, H **, B He, L Zheng… - Proceedings of the …, 2017 - dl.acm.org
Non-Volatile Memory (NVM) has recently emerged for its nonvolatility, high density and
energy efficiency. Hybrid memory systems composed of DRAM and NVM have the best of …