February 24, 2021
MTL Seminar Series

IBM Analog Hardware Acceleration Kit: A Flexible and Fast PyTorch Toolkit for Simulating ANN Training and Inference on Resistive Crossbar Arrays

Malte Rasch, IBM
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Malte Rasch graduated in biophysics and computational neuroscience(Humboldt University Berlin, TU Graz) and previously was associate professor at the Beijing Normal University. He moved to IBM Research AI in 2017 to work on the algorithmic aspects of analog AI and to develop simulation tools.

Memristive crossbar arrays are a promising future technology for accelerating AI workloads, but noise and non-idealities demand for improved algorithmic solutions. We introduce the IBM Analog Hardware Acceleration Kit, a first of a kind open source toolkit to simulate crossbar arrays from within PyTorch, to conveniently estimate the impact of material properties and non-idealities on the accuracy for arbitrary ANNs.

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