2026-06-13T03:01:39Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/454302021-11-08T09:08:19Zcom_10324_43510com_10324_954com_10324_894col_10324_43515

Milo, V. 2d389efc-2bb5-473f-ab2a-dc8b7666f125 500 Zambelli, Cristian 2be552fe-da21-4bdb-bfd1-78dcba9cf377 500 Olivo, P. 050341ec-d54c-4fed-9731-24d70ebb3378 500 Pérez, Eduardo f19d8afc-bcc9-4963-9681-d44bd8382f38 500 González Ossorio, Óscar 81051a2cf33e203e 500 Wenger, Christian 568e8108-86a6-4e67-bca0-88ed2c6b1f73 500 Ielmini, Daniele 49c9d6be-eb5a-47cc-8ce2-40332548c5ff 500 2021-03-02T08:42:19Z 2021-03-02T08:42:19Z 2019 49th European Solid-State Device Research Conference (ESSDERC 2019). Cracovia, Polonia: IEEE Xplore, 2019, p. 174-177 978-1-7281-1539-9 http://uvadoc.uva.es/handle/10324/45430 10.1109/ESSDERC.2019.8901818 Producción Científica Recently, artificial intelligence reached impressive milestones in many machine learning tasks such as the recognition of faces, objects, and speech. These achievements have been mostly demonstrated in software running on high-performance computers, such as the graphics processing unit (GPU) or the tensor processing unit (TPU). Novel hardware with in-memory processing is however more promising in view of the reduced latency and the improved energy efficiency. In this scenario, emerging memory technologies such as phase change memory (PCM) and resistive switching memory (RRAM), have been proposed for hardware accelerators of both learning and inference tasks. In this work, a multilevel 4kbit RRAM array is used to implement a 2-layer feedforward neural network trained with the MNIST dataset. The performance of the network in the inference mode is compared with recently proposed implementations using the same image dataset demonstrating the higher energy efficiency of our hardware, thanks to low current operation and an innovative multilevel programming scheme. These results support RRAM technology for in-memory hardware accelerators of machine learning. German Research Foundation (grant FOR2093) application/pdf eng IEEE Xplore info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/ https://ieeexplore.ieee.org/document/8901818 Attribution-NonCommercial-NoDerivatives 4.0 Internacional Resistive RAM memory (RRAM) Memoria RAM resistiva (RRAM) Machine learning Aprendizaje automatizado Neural network Red neuronal 1203.04 Inteligencia Artificial Low-energy inference machine with multilevel HfO2 RRAM arrays 49th European Solid-State Device Research Conference (ESSDERC 2019) info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/publishedVersion info:eu-repo/grantAgreement/EC/H2020/648635 10324/45430 oai:uvadoc.uva.es:10324/45430 2021-11-08 10:08:19.603 UVaDOC repositorio@uva.es