Energy Efficient Implementation of MVM Operations Using Filament-Free Bulk RRAM Array

Ashwani Kumar, Jaeseoung Park, Yucheng Zhou, Jeong-Hoon Kim, Soumil Jain, Catherine D. Schuman, Gert Cauwenberghs, Duygu Kuzum

Abstract: In this work, we present a hardware implementation of spiking neural network (SNN) model using trilayer bulk RRAM crossbar arrays for an autonomous navigation/racing task. We demonstrate multi-level switching in MΩ regime using trilayer (Al2O3/TiO2/TiOx) bulk RRAM devices without needing a compliance current. We also present a neuromorphic compute-in-memory (CIM) architecture based on trilayer bulk RRAM crossbars using energy-efficient voltage sensing and row differential encoding of weights to experimentally implement highly accurate matrix-vector-multiplications (MVM). Our results suggest that use of our bulk RRAM can reduce the energy consumption by more than 100× compared to other filamentary RRAMs. This work paves the way for neuromorphic computing at the edge under strict area and energy constraints.