Introduction to AIHWKIT, a Simulation Platform for Next Generation Analog AI Hardware Acceleration

Presenter: Dr. Kaoutar El Maghraoui, Principal Research Staff Member, AI Engineering, IBM Research AI

Kaoutar El Maghraoui

Kaoutar El Maghraoui

Abstract: The next step in the evolution of specialized hardware for AI is rooted in addressing the performance efficiency loss from data movement between computational units and memory. This can be achieved through analog in-memory computing which eliminates the Von Neuman bottleneck and allows highly parallel computations directly in memory using memristive crossbar arrays. Although memristive crossbar arrays are a promising future Analog technology for accelerating AI workloads, their inherent noise, and non-idealities demand for improved algorithmic solutions.

We introduce the IBM Analog Hardware Acceleration Kit 1, 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 (freely available at This platform allows understanding, evaluating, and experimenting with emerging analog AI accelerators. Our roadmap and capabilities include algorithmic innovations from IBM Research around hardware-aware training, mixed-precision training, advanced analog training optimizers using parallel rank-update in analog, and allowing inference on real research Phase-change memory (PCM)-based analog AI chip prototypes, as well as allowing the research community to extend the toolkit with new devices, analog presets, algorithms, etc.

We will show an interactive demo of how the toolkit can be used online though our web front-end cloud composer. The composer provides a set of templates and a no-code experience to introduce the concepts of analog AI, configure experiments, and launch training experiments. We are actively working to include inference experiments in simulation and a real PCM-based analog AI chip.

Speaker Bio: Dr. Kaoutar El Maghraoui is a principal research staff member at the IBM T.J Watson Research Center where she is focusing on innovations at the intersection of systems and artificial intelligence (AI). She leads the AI testbed of the IBM Research AI Hardware Center, a global research hub focusing on enabling next-generation accelerators and systems for AI workloads. Kaoutar has co-authored several patents, conference, and journal publications in the areas of systems research, distributed systems, high performance computing, and AI. Kaoutar holds a PhD. degree from Rensselaer Polytechnic Institute, USA. She received several awards including the Robert McNaughton Award for best thesis in computer science, Best of IBM award in 2021, IBM’s Eminence and Excellence award for leadership in increasing Women’s presence in science and technology, several IBM outstanding technical accomplishments, and 2021 IEEE TCSVC Women in Service Computing award.

  1. Malte J. Rasch, Diego Moreda, Tayfun Gokmen, Manuel Le Gallo, Fabio Carta, Cindy Goldberg, Kaoutar El Maghraoui, Abu Sebastian; Vijay Narayanan, “A Flexible and Fast PyTorch Toolkit for Simulating Training and Inference on Analog Crossbar Arrays” 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2021, pp. 1-4. ↩︎