Thermal-aware optimizations of reRAM-based neuromorphic computing systems-Reference-Cited by-同舟云学术

Thermal-aware optimizations of reRAM-based neuromorphic computing systems

Published:2018-06-24 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 55th Annual Design Automation Conference
language:
Short-container-title:

Author:

Beigi Majed Valad¹,Memik Gokhan¹

Affiliation:

1. Northwestern University

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3195970.3196128

Reference20 articles.

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2. G. W. Burr etal "Experimental demonstration and tolerancing of a large-scale neural network (165 000 synapses) using phase-change memory as the synaptic weight element " in IEDM 2014. G. W. Burr et al. "Experimental demonstration and tolerancing of a large-scale neural network (165 000 synapses) using phase-change memory as the synaptic weight element " in IEDM 2014.

3. L. Chen etal "Accelerator-friendly neural-network training: learning variations and defects in RRAM crossbar " in DATE 2017. L. Chen et al. "Accelerator-friendly neural-network training: learning variations and defects in RRAM crossbar " in DATE 2017.

4. P.-Y. Chen etal "NeuroSim+: An integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures " in IEDM 2017. P.-Y. Chen et al. "NeuroSim+: An integrated device-to-algorithm framework for benchmarking synaptic devices and array architectures " in IEDM 2017.

5. D. C. Ciresan etal "Convolutional Neural Network Committees for Handwritten Character Classification " in ICDAR 2011. 10.1109/ICDAR.2011.229 D. C. Ciresan et al. "Convolutional Neural Network Committees for Handwritten Character Classification " in ICDAR 2011. 10.1109/ICDAR.2011.229

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