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Titel:

Exploiting Inhomogeneities of Subthreshold Transistors as Populations of Spiking Neurons

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Mueller, Etienne; Auge, Daniel; Knoll, Alois
Abstract:
As machine learning applications are becoming increasingly more powerful and are deployed to a growing number of different devices, the need for energy-efficient implementations is rising. To meet this de- mand, a promising field of research is the use of spiking neural networks in combination with neuromorphic hardware, as energy is only consumed when information is being processed. The approach that maximizes en- ergy efficiency, an analog layout with transistors operating in subthresh-...     »
Stichworte:
Spiking neural networks, conversion, long short-term memory, subthreshold analog neuromorphic hardware
Kongress- / Buchtitel:
International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)
Jahr:
2022
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