tinyML Talks: Tools and Methodologies for Edge-AI Mixed-Signal Inference Accelerators

Tools and Methodologies for Edge-AI Mixed-Signal Inference Accelerators
Maen Mallah
Senior engineer
Fraunhofer Institute for Integrated Circuits IIS

Roland Müller
Senior engineer
Fraunhofer Institute for Integrated Circuits IIS

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.

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