2018

[1] C. Liu, G. Bellec, B. Vogginger, D. Kappel, J. Partzsch, F. Neumärker, S. Höppner, Wo. Maass, S. Furber, R. Legenstein and C. Mayr. Memory-Efficient Deep Learning on a SpiNNaker 2 Prototype.In Frontiers of  Neuroscience Vol. 12, November 2018, article  840

[2] H. Keren, J. Partzsch, S. Marom, and C. Mayr. Closed-loop control of a modular neuromorphic biohybrid. Cornell University, Feb. 2018

[3] S. Rai, A. Rupani, D. Walter, M. Raitza, A. Heinzig, T. Baldauf,  J. Trommer, C. Mayr,   W. Weber, A. Kumar. A Physical Synthesis Flow for Early Technology Evaluation of Silicon Nanowire based Reconfigurable FETs. In 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE), 19-23 March 2018, pp. 605-608

[4] E. Covi, R. George, J. Frascaroli, S. Brivio, C. Mayr, H. Mostafa, G. Indiveri, S. Spiga. Spike-driven threshold-based learning with memristive synapses and neuromorphic silicon neurons. In Journal of Physics D: Applied Physics 51 (2018) 344003 (11pp)

[5] P. Chávez, J. Schreiter, S. Siegmund, C. Mayr. A continuation approach for computing parameter-dependent separatrices in SRAM cells. In ScienceDirect,  July 2018.

[6] S. Höppner, J. Partzsch, C. Mayr, S. Furber. SpiNNaker2-An Energy efficient realtime neuromorphic compute system in 22FDX technology. In Arm Research Summit 2018

[7] S. Höppner, C. Mayr: SpiNNaker2-Towards Extremely Efficient Digital Neuromorphics and Multi-scale Brain Emulation. In Proc. Neuro Inspired Comput. Elements Workshop, 1-21

[8] M. Mikatis, D. Lester, D. Shang, S. Furber, G. Liu, J. Garside, S. Scholze, S. Höppner, A. Dixius, Approximate fixed-point elementary function accelerator for the SpiNNaker-2 Neuromorphic Chip, 2018 IEEE 25th Symposium on Computer Artithmetic (ARITH), 37-44

[9] S Höppner, F Neumärker, A Dixius, Method for generating true random numbers on a multiprocessor system and the same, US Patent 9,959,096