3 citations · 5 across the 4 of their papers we have counts for
4 papers
Rethinking Neural Nonlinearity as Gating
Muhammad Sabih, Frank Hannig, Jürgen Teich
Activation functions are considered an essential primitive for neural nonlinearity, i.e., they enable neural networks to serve as universal approximators. In this paper, we show th…
Co-Design of CNN Accelerators for TinyML using Approximate Matrix Decomposition
José Juan Hernández Morales, Georgios Mentzos, Frank Hannig +4
The paradigm shift towards local and on-device inference under stringent resource constraints is represented by the tiny machine learning (TinyML) domain. The primary goal of TinyM…
OpTC -- A Toolchain for Deployment of Neural Networks on AURIX TC3xx Microcontrollers
Christian Heidorn, Frank Hannig, Dominik Riedelbauch +2
The AURIX 2xx and 3xx families of TriCore microcontrollers are widely used in the automotive industry and, recently, also in applications that involve machine learning tasks. Yet,…
Energy-efficient Deployment of Deep Learning Applications on Cortex-M based Microcontrollers using Deep Compression
Mark Deutel, Philipp Woller, Christopher Mutschler +1
Large Deep Neural Networks (DNNs) are the backbone of today's artificial intelligence due to their ability to make accurate predictions when being trained on huge datasets. With ad…