1 code implementation • 18 Jul 2023 • Manuel Le Gallo, Corey Lammie, Julian Buechel, Fabio Carta, Omobayode Fagbohungbe, Charles Mackin, Hsinyu Tsai, Vijay Narayanan, Abu Sebastian, Kaoutar El Maghraoui, Malte J. Rasch
In this tutorial, we provide a deep dive into how such adaptations can be achieved and evaluated using the recently released IBM Analog Hardware Acceleration Kit (AIHWKit), freely available at https://github. com/IBM/aihwkit.
no code implementations • 8 Mar 2023 • Malte J. Rasch, Fabio Carta, Omebayode Fagbohungbe, Tayfun Gokmen
In-memory computing with resistive crossbar arrays has been suggested to accelerate deep-learning workloads in highly efficient manner.
1 code implementation • 5 Apr 2021 • Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta, Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian, Vijay Narayanan
We introduce the IBM Analog Hardware Acceleration Kit, a new and first of a kind open source toolkit to simulate analog crossbar arrays in a convenient fashion from within PyTorch (freely available at https://github. com/IBM/aihwkit).
