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

Wednesday, February 24, 2021

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

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.

Speaker: Malte Rasch, IBM

Explore

Futuristic battery with “lightning bolt” icon, and a honeycomb lattice background.

Discovery helps explain why solid-state batteries often fail

Zach Winn | MIT News

New research could help prevent the formation of tiny seeds of lithium metal within the electrolyte, enabling batteries that charge faster and last longer.

Energy Efficient AIMaterials