Energy-Efficient SNN Implementation Using RRAM-Based Computation In-Memory (CIM)-Reference-Cited by-同舟云学术

Energy-Efficient SNN Implementation Using RRAM-Based Computation In-Memory (CIM)

Published:2022-10-03 Issue: Volume: Page:
ISSN:
Container-title:2022 IFIP/IEEE 30th International Conference on Very Large Scale Integration (VLSI-SoC)
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Author:

Arrassi Asmae El¹,Gebregiorgis Anteneh²,Haddadi Anass El¹,Hamdioui Said²

Affiliation:

1. Abdelmalek Essaadi University,ENSA,Al-Hoceima,Morocco

2. Delft University of Technology,Department of Quantum and Computer Engineering,Delft,The Netherlands

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9939277/9939284/09939654.pdf?arnumber=9939654

Reference51 articles.

1. The characteristics of binary spike-time-dependent plasticity in hfo 2-based rram and applications for pattern recognition;zhou;Nanoscale Research Letters,2017

2. Crossbar-Level Retention Characterization in Analog RRAM Array-Based Computation-in-Memory System

3. Efficient Optimized Spike Encoding of Multivariate Time-series

4. An area and energy efficient LIF neuron model with spike frequency adaptation mechanism

5. RRAM for Compute-in-Memory: From Inference to Training

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