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NEURO-INSPIRED QUANTIZATION

Abstract : This paper presents a novel neuro-inspired quantization model which is the extension of the recently released perfect-Leaky Integrate and Fire (LIF) model. We propose that the LIF, which is a very efficient neuromathematical model that describes the spike generation neural mechanism, can lead to a groundbreaking and above all dynamic compression algorithm which is called LIF encoder/decoder. We also prove that under some assumptions, there is a link between the novel LIF encoder/decoder and the conventional Uniform Deadzone Quantizer (UDQ).
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https://hal.archives-ouvertes.fr/hal-02144872
Contributor : Effrosyni Doutsi <>
Submitted on : Friday, January 8, 2021 - 2:46:36 PM
Last modification on : Monday, January 18, 2021 - 5:11:30 PM
Long-term archiving on: : Friday, April 9, 2021 - 7:07:54 PM

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Effrosyni Doutsi, Lionel Fillatre, Marc Antonini, Julien Gaulmin. NEURO-INSPIRED QUANTIZATION. 25th IEEE International Conference on Image Processing (ICIP 2018), Oct 2018, Athènes, Greece. pp.689-693, ⟨10.1109/ICIP.2018.8451793⟩. ⟨hal-02144872⟩

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