11am-noon CET



The “memory wall” challenge in traditional digital systems has been aggravated by the explosive growth in data-hungry applications. A radically new approach is analog in-memory computing, where certain computational tasks are performed in an analog manner in the memory itself. Given the precision of analog matrix-vector operations that can be performed on arrays of analog devices, analog in-memory computing is particularly well suited when approximate solutions are acceptable, such as many AI tasks. Furthermore, new computing paradigms such as hyperdimensional computing are lending themselves extraordinarily well to the inherent variability of analog in-memory computing. This talk will illustrate these exciting new research directions with efforts undertaken at IBM Research Europe in Zurich.


  • Introduction, Michael Malms, ETP4HPC (5 min)
  • In-memory Computing for AI Applications, Robert Haas, IBM (30 min)
  • ETP4HPC’s webinar plans for 2021, Maria Toth, ETP4HPC (5 min)
  • Q&As (20min)


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