Plimpton, Steven J.

LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

Thompson, A. P., Aktulga, H. M., Berger, R., Bolintineanu, D. S., Brown, W. M., Crozier, P. S., in 't Veld, P. J., Kohlmeyer, A., Moore, S. G., Nguyen, T. D., et al. (2022) · Computer Physics Communications

Parallel Computing and Optimization TechniquesAdvanced Physical and Chemical Molecular Interactions

Cited by 12,598

Resistive memory device requirements for a neural algorithm accelerator

Agarwal, S., Plimpton, S. J., Hughart, D. R., Hsia, A. H., Richter, I., Cox, J. A., James, C. D., & Marinella, M. J. (2016) · 2016 International Joint Conference on Neural Networks (IJCNN)

Advanced Memory and Neural ComputingNeural Networks and Applications

Cited by 213

Multiscale Co-Design Analysis of Energy, Latency, Area, and Accuracy of a ReRAM Analog Neural Training Accelerator

Marinella, M. J., Agarwal, S., Hsia, A., Richter, I., Jacobs-Gedrim, R., Niroula, J., Plimpton, S. J., Ipek, E., & James, C. D. (2018) · IEEE Journal on Emerging and Selected Topics in Circuits and Systems

Advanced Memory and Neural ComputingLow-power high-performance VLSI design

Cited by 183

Direct simulation Monte Carlo: The quest for speed

Gallis, M. A., Torczynski, J. R., Plimpton, S. J., Rader, D. J., & Koehler, T. (2014) · AIP Conference Proceedings

Gas Dynamics and Kinetic TheoryLow-Pressure Plasma Processing and Diagnostics

Cited by 147