Hardware implementation of memristor-based artificial neural networks
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 …
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
Deep Learning (DL) systems have demonstrated unparalleled performance in many
challenging engineering applications. As the complexity of these systems inevitably …
challenging engineering applications. As the complexity of these systems inevitably …
Characterizing and modeling non-volatile memory systems
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 …
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
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 …
focus on hardware or application-level perspectives, the authors elaborate on architectures …
GraphA: An efficient ReRAM-based architecture to accelerate large scale graph processing
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 …
streaming data problems. With an unprecedented increase in data size of many emerging …
NEUTRAMS: Neural network transformation and co-design under neuromorphic hardware constraints
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 …
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 …
Research Directions for realizing the capabilities needed to address the challenges posed …
Supermem: Enabling application-transparent secure persistent memory with low overheads
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 …
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
Self-attention-based transformer models have achieved tremendous success in the domain
of natural language processing. Despite their efficacy, accelerating the transformer is …
of natural language processing. Despite their efficacy, accelerating the transformer is …
Hardware/software cooperative caching for hybrid DRAM/NVM memory architectures
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 …
energy efficiency. Hybrid memory systems composed of DRAM and NVM have the best of …