122 citations · 396 across the 10 of their papers we have counts for
5 papers · 1 filter
Integration of Physics-Derived Memristor Models with Machine Learning Frameworks
Zhenming Yu, Stephan Menzel, John Paul Strachan +1
Simulation frameworks such MemTorch, DNN+NeuroSim, and aihwkit are commonly used to facilitate the end-to-end co-design of memristive machine learning (ML) accelerators. These simu…
NeuroHammer: Inducing Bit-Flips in Memristive Crossbar Memories
Felix Staudigl, Hazem Al Indari, Daniel Schön +6
Emerging non-volatile memory (NVM) technologies offer unique advantages in energy efficiency, latency, and features such as computing-in-memory. Consequently, emerging NVM technolo…
2022 Roadmap on Neuromorphic Computing and Engineering
Dennis V. Christensen, Regina Dittmann, Bernabé Linares-Barranco +56
Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as sep…
A Complementary Resistive Switch-based Crossbar Array Adder
A. Siemon, S. Menzel, R. Waser +1
Redox-based resistive switching devices (ReRAM) are an emerging class of non-volatile storage elements suited for nanoscale memory applications. In terms of logic operations, ReRAM…
Applicability of Well-Established Memristive Models for Simulations of Resistive Switching Devices
E. Linn, A. Siemon, R. Waser +1
Highly accurate and predictive models of resistive switching devices are needed to enable future memory and logic design. Widely used is the memristive modeling approach considerin…