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

00925njm 22002777a 4500 dc Milo, V. author Zambelli, Cristian author Olivo, P. author Pérez, Eduardo author González Ossorio, Óscar author Wenger, Christian author Ielmini, Daniele author 2019 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. 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 Resistive RAM memory (RRAM) Memoria RAM resistiva (RRAM) Machine learning Aprendizaje automatizado Neural network Red neuronal Low-energy inference machine with multilevel HfO2 RRAM arrays