Are SNNs Really More Energy-Efficient Than ANNs? an In-Depth Hardware-Aware Study

Author:

Dampfhoffer Manon1ORCID,Mesquida Thomas2,Valentian Alexandre2,Anghel Lorena1ORCID

Affiliation:

1. CEA, CNRS, Grenoble INP, INAC-Spintec, University Grenoble Alpes, Grenoble, France

2. CEA, List, University Grenoble Alpes, Grenoble, France

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computational Mathematics,Control and Optimization,Computer Science Applications

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. MiCE: An ANN-to-SNN Conversion Technique to Enable High Accuracy and Low Latency;IEEE Journal on Emerging and Selected Topics in Circuits and Systems;2023-12

2. An Area-Power-Efficient Multiplier-less Processing Element Design for CNN Accelerators;2023 IEEE 15th International Conference on ASIC (ASICON);2023-10-24

3. FireFly: A High-Throughput Hardware Accelerator for Spiking Neural Networks With Efficient DSP and Memory Optimization;IEEE Transactions on Very Large Scale Integration (VLSI) Systems;2023-08

4. 1S1R Sub-Threshold Operation in Crossbar Arrays for Neural Networks Hardware Implementation;2023 30th International Conference on Mixed Design of Integrated Circuits and System (MIXDES);2023-06-29

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