TY - GEN A1 - Glukhov, Artem A1 - Milo, Valerio A1 - Baroni, Andrea A1 - Lepri, Nicola A1 - Zambelli, Cristian A1 - Olivo, Piero A1 - Perez, Eduardo A1 - Wenger, Christian A1 - Ielmini, Daniele T1 - Statistical model of program/verify algorithms in resistive-switching memories for in-memory neural network accelerators T2 - 2022 IEEE International Reliability Physics Symposium (IRPS) N2 - Resistive-switching random access memory (RRAM) is a promising technology for in-memory computing (IMC) to accelerate training and inference of deep neural networks (DNNs). This work presents the first physics-based statistical model describing (i) multilevel RRAM device program/verify (PV) algorithms by controlled set transition, (ii) the stochastic cycle-to-cycle (C2C) and device-to-device (D2D) variations within the array, and (iii) the impact of such imprecisions on the accuracy of DNN accelerators. The model can handle the full chain from RRAM materials/device parameters to the DNN performance, thus providing a valuable tool for device/circuit codesign of hardware DNN accelerators. KW - RRAM KW - Multilevel switching KW - neural network Y1 - 2022 UR - https://opus4.kobv.de/opus4-UBICO/frontdoor/index/index/docId/28821 SN - 978-1-6654-7950-9 SN - 978-1-6654-7951-6 SN - 2473-2001 SP - 3C.3-1 EP - 3C.3-7 PB - Institute of Electrical and Electronics Engineers (IEEE) ER -