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Article Dans Une Revue Solid-State Electronics Année : 2023

A tunable and versatile 28nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses

Kevin Garello
Lorena Anghel

Résumé

In this work we report a study and a co-design methodology of an analog SNN crossbar output circuit designed in a 28nm FD-SOI technology node that comprises a tunable current attenuator and a leak-integrate and fire neurons that would enable the integration of emerging non-volatile memories (eNVMs) for synaptic arrays based on various technologies including phase change (PCRAM), oxide-based (OxRAM), spin transfer and spin orbit torque magnetic memories (STT, SOT-MRAM). Circuit SPICE simulation results and eNVM experimental data are used to showcase and estimate the neurons fan-in for each type of eNVM considering the technology constraints and design trade-offs that set its limits such as membrane capacitance and supply voltage, etc.
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Dates et versions

hal-04221623 , version 1 (28-09-2023)

Identifiants

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Joao Henrique Quintino Palhares, Yann Beilliard, Jury Sandrini, Franck Arnaud, Kevin Garello, et al.. A tunable and versatile 28nm FD-SOI crossbar output circuit for low power analog SNN inference with eNVM synapses. Solid-State Electronics, 2023, 209, pp.108779. ⟨10.1016/j.sse.2023.108779⟩. ⟨hal-04221623⟩
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