Implementation of offset corrected AGAD algorithm on 130-nm CMOS technology–based RRAM array for analog neural network training

Jimin Lee; Paul Michael Solomon; Nanbo Gong; Omobayode I. Fagbohungbe; Russel Wilson; Takashi Ando; Seyoung Kim

doi:10.1126/sciadv.aee7134

IA y aprendizaje automáticoEficiencia y hardware

Implementation of offset corrected AGAD algorithm on 130-nm CMOS technology–based RRAM array for analog neural network training

Jimin Lee, Paul Michael Solomon, Nanbo Gong, Omobayode I. Fagbohungbe, Russel Wilson, Takashi Ando, Seyoung Kim

Revista con revisión por paresAfirmaciones fuertes, leer con cuidadoDice ser un gran avance

En palabras de los autores

Esta revista no permite republicar el resumen completo. Estas son las dos oraciones que eligió Pipette, citadas textualmente. El resto está en el sitio de la editorial.

Resultado principal

We present the first implementation of the analog gradient accumulation with dynamic reference (AGAD), reported as the most advanced and highest-performing version of the TT (Tiki-Taka) algorithm, on an HfO 2 -based resistive random-access memory (RRAM) array for analog neural network training.

Leer el paper

Apareció: domingo, 27 de septiembre. Science Advances. Revista con revisión por pares.

DOI: 10.1126/sciadv.aee7134

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