tinyML Talks on December 12, 2023 “Tools and Methodologies for Edge-AI Mixed-Signal Inference Accelerators” by Maen Mallah and Roland Müller from Fraunhofer Institute for Integrated Circuits IIS

We held our next tinyML Talks webcast. Maen Mallah and Roland Müller from Fraunhofer Institute for Integrated Circuits IIS presented tinyML: Designing Efficient Neural Architectures and Scaling Strategies for Edge Computing on December 12, 2023.

A major obstacle for the Adoption of NN on the edge and near the sensors due to their high computational requirements. Our approach at Fraunhofer is to develop novel neuromorphic mixed-signal edge-AI accelerators. This approach comes with challenges that require hardware/software co-design and a dedicated workflow. For this purpose, we developed several tools to facilitate design, training and deployment of artificial neural networks in dedicated hardware accelerators. These tools provide hardware-aware training, automatic hardware generation, compilers, estimation of KPIs like energy consumption, and simulation under consideration of the constraints imposed by the targeted hardware implementation and use cases. The development of such a tool chain is a multidisciplinary effort combining neural network algorithm design, software development and integrated circuit design. We show how such a toolchain allows to optimize and verify the hardware design, reach the targeted KPIs, and reduce the time-to-market.

Mallah is a researcher in the area of embedded AI at the Fraunhofer Institute for Integrated Circuits (IIS). He obtained his B.Sc in Telecommunication Engineering in 2014 from An-Najah National University, Palestine and his M.Sc in Electrical Engineering in 2018 from Bilkent University, Turkey with a thesis titled “Multiplication Free Neural Networks”. In March 2018, He joined Fraunhofer IIS as an expert for eAI and focusing mainly on energy efficient NNs. His main work and interest focuses on implementing and optimizing NNs for Edge applications and designing the special SW tools required for such a task with a special focus on Quantization- and Fault-aware training.

Müller obtained his bis B.Eng. at the OTH Regensburg in 2017 and his M.Sc. in 2019 at the FAU Erlangen, both in electrical engineering. In May 2019, he joined the department of Integrated Circuits and Systems at Fraunhofer IIS, Erlangen (Germany), where he is working in the field of analog-mixed signal design of neural network accelerators and design automation for such circuits. Currently, he is pursuing his PhD. His main research interests include low power analog-mixed signal circuits, neuromorphic computing and electronic design automation.


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Maen Mallah and Roland Müller

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Maen Mallah and Roland Müller

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