The DeepChip Project - Deep Learning for Resource-Constrained Systems


DeepChip focusses on deep learning techniques for resource-constrained systems. Many processes require evaluation of complex numerical functions close to the machine or structure of interest, to avoid the effort of data transfer or to enable small reaction times. Although computing performance of embedded platforms is increasing, it is often significantly lower than the requirements of state-of-the-art algorithms. With the advent of Deep Neural Networks (DNN), the achievable classification performance has been pushed to new levels. The high cost of execution, however, renders them unusable to many real-world applications. A possible approach is the use of hybrid processors (ARM+FPGA or similar), but this raises the question on how to auto-generate optimized DNN classifier implementations. In the DeepChip project, we tackle this problem by optimizing deep models in terms of sparsity, asynchrony and reduced precision, and by extending machine learning languages with a hybrid back-end that is responsible for code generation, automated partitioning and integration.

The DeepChip Project was a FWF/DFG co-funded D-A-CH project, run by Graz University of Technology and Heidelberg University. While the first partner contributes expertise and experience from the machine learning area and applications, the second partner has a strong background on application-specific computing systems of various scale. Within the DeepChip project, the partners jointly pursue the objective of designing a productive and easy-to-use tool chain to design custom hardware for deep learning purposes, thereby contributing to bringing advanced machine learning techniques and principles to tiny embedded devices like mobile chips, Internet of Things and more.

Meantime, the term DeepChip serves as an umbrella for various activities in the context of embedded machine learning, with a particular focos on HW systems for machine learning, HW/SW codesign, and emerging technologies for learning-based applications.

For questions or comments, please contact: Holger Fröning, holger.froening (at)

Workshop mini-series on Embedded Machine Learning (WEML)

We are frequently hosting rather informal workshops that gather experts and interested people in machine learning, particulary deep learning, for embedded or other resource-constrained systems. More informations about recent incarnations can be found here:

Note that the next edition is postponed until the COVID situation allows for a better planning of an in-presence event.

Workshop mini-series on IoT, Edge, and Mobile for Embedded Machine Learning (ITEM)

While WEML is a rather informal gathering with no proceedings or similar, ITEM is its academic counterpart, collocated ususally with ECML-PKDD as a premier European forum on ML. For more information about recent and upcoming editions, please visit The next edition is scheduled for September 2022.


The DeepChip project is co-run by the

Current people:

  • Holger Fröning, Co-PI, Heidelberg University, Germany

  • Franz Pernkopf, Co-PI, Graz University of Technology, Austria

  • Bernhard Klein, PhD student, Heidelberg University, Germany

  • Yannick Emonds, PhD student, Heidelberg University, Germany

  • Hendrik Borras, PhD student, Heidelberg University, Germany

  • Dennis Rieber, PhD student, Bosch, Germany

Associated partners

  • Manfred Mücke, Materials Center Leoben, Austria

Former people:

  • Matthias Zöhrer, Graz University of Technology, Austria

  • Günther Schindler, Heidelberg University, Germany

  • Wolfgang Roth, Graz University of Technology, Austria


[ACCML2022] Dennis Rieber, Moritz Reiber, Oliver Bringmann and Holger Fröning, HW-Aware Initialization of DNN Auto-Tuning to Improve Exploration Time and Robustness, 4th Accelerated Machine Learning (AccML) workshop at HiPEAC 2022, Budapest, Hungary, June 22, 2022. (accepted for publication)

[ECMLW2021] Bernhard Klein, Lisa Kuhn, Johannes Weis, Arne Emmel, Yannik Stradmann, Johannes Schemmel, and Holger Fröning, Towards Addressing Noise and Static Variations of Analog Computations Using Efficient Retraining. Kamp M. et al. (eds) Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2021. Communications in Computer and Information Science, vol 1524, Springer.

[TACO2022] Dennis Rieber, Axel Acosta, and Holger Fröning, Joint Program and Layout Transformations to Enable Convolutional Operators on Specialized Hardware Based on Constraint Programming, ACM Transactions on Architecture and Code Optimization (TACO), Volume 19, Issue 1 (March 2022).

[ACCML2021] Bernhard Klein, Christoph Gratl, Manfred Mücke and Holger Fröning, Understanding Cache Boundness of ML Operators on ARM Processors, 3rd Workshop on Accelerated Machine Learning (AccML), co-located with HiPEAC 2021 Conference, Jan 19, 2021 (online). [github]

Günther Schindler, Compressing and Mapping Deep Neural Networks on Edge Computing Systems, Dissertation, Heidelberg University, July 19, 2021. [doi]

[ICPR2021] Wolfgang Roth, Günther Schindler, Holger Fröning, and Franz Pernkopf, On Resource-Efficient Bayesian Network Classifiers and Deep Neural Networks, 25th International Conference on Pattern Recognition (ICPR2021), Milan, Italy, Jan. 2021.

[LOD2020] Günther Schindler, Wolfgang Roth, Franz Pernkopf and Holger Fröning, Parameterized Structured Pruning for Deep Neural Networks, 6th International Conference on Machine Learning, Optimization, and Data Science (LOD 2020), July 19-23, 2020, Siena, Italy. Best paper finalist.

[ICASSP2020] Markus Huber, Günther Schindler, Wolfgang Roth, Holger Fröning, Christian Schörkhuber, Franz Pernkopf, Towards Real-Time Single-Channel Single-Voice Separation with Pruned Multi-Scale DenseNets, 45th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Barcelona, May 4-8, 2020.

[ARXIV] Wolfgang Roth, Günther Schindler, Matthias Zöhrer, Lukas Pfeifenberger, Robert Peharz, Sebastian Tschiatschek, Holger Fröning, Franz Pernkopf, Zoubin Ghahramani, Resource-Efficient Neural Networks for Embedded Systems. ArXiv:2001.03048 [stat.ML], Jan. 2020.

[ECML2019] Wolfgang Roth, Günther Schindler, Holger Fröning, Franz Pernkopf, Training Discrete-Valued Neural Networks with Sign Activations Using Weight Distributions, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2019), Sept. 16-20, Würzburg, Germany. (acceptance rate: 17.7%, 130/734)

[CoRR.cs] Günther Schindler, Wolfgang Roth, Franz Pernkopf, Holger Fröning, Parameterized Structured Pruning for Deep Neural Networks, arXiv:1906.05180 [CoRR.cs], June 2019.

[HiPEAC2019EDLA] Christoph Gratl, Manfred Mücke, Günther Schindler and Holger Fröning, Towards efficient mapping of BNNs onto embedded targets using Tensorflow/XLA, 1st Workshop on Emerging Deep Learning Accelerators (EDLA), co-located with the HiPEAC 2019 Conference, January 21-23, 2019, Valencia, Spain.

[CoRR.cs] Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer, Lukas Pfeifenberger, Günther Schindler, Holger Fröning, Sebastian Tschiatschek, Robert Peharz, Matthew Mattina, Zoubin Ghahramani, Efficient and Robust Machine Learning for Real-World Systems, arXiv:1812.02240 [CoRR.cs], December 2018.

[ECML2018] Günther Schindler, Matthias Zöhrer, Franz Pernkopf, and Holger Fröning, Towards Efficient Forward Propagation on Resource-Constrained Systems, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2018), Sept 10-14, Dublin, Ireland. (acceptance rate: 26%, 92/354)

[ICASSP2018] Matthias Zöhrer, Lukas Pfeifenberger, Günther Schindler, Holger Fröning, and Franz Pernkopf, Resource Efficient Deep Eigenvector Beamforming, 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 15–20 April 2018, Calgary, Alberta, Canada.

[UCHPC2017] Günther Schindler, Manfred Mücke, Holger Fröning, Linking Application Description with Efficient SIMD Code Generation for Low-Precision Signed-Integer GEMM, 10th Workshop on UnConventional High Performance Computing 2017 (UCHPC 2017), in conjunction with EuroPAR 2017, August 28/29, 2017, Santiago de Compostela, Spain.


Please check the Computing Systems Group's github site for various projects:


We gratefully acknowledge the sponsoring we receive from the DFG, FWF, FFG, Carl-Zeiss Foundation, and various others.