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The 3rd Workshop on Embedded Machine Learning (WEML) took place at Heidelberg University on 13 February 2020, attracting about 60 attendees. This workshop series is jointly organized by Heidelberg University (Holger Fröning), Graz University of Technology (Franz Pernkopf) and Materials Center Leoben (Manfred Mücke), and embraces joint interest in bridging the gap between complex machine learning models and methods to resource-constrained devices like edge devices, embedded devices, and the Internet of Things (IoT). The workshop focuses on invited presentations, with ample time for discussions and other interactions. This time, the program included speakers from Robert Bosch GmbH, XILINX Research, NEC Laboratories Europe, Graz University of Technology, Materials Center Leoben, and Heidelberg University. The workshop started with a focus on tooling, including an overview about resource-efficiency in deep learning, with methods such as pruning, quantization and others, followed by a deep dive into network pruning for specific hardware. Further contributions included an update on code generation for embedded targets, and an overview of machine learning optimization tools for specialized architectures. The following part focused on hardware, with presentations on using neuromorphic hardware for deep learning, and quantization-aware training for field-programmable gate arrays (FPGAs). The last part was dedicated to “Beyond-CNN” models, including Graph-based Neural Networks, Sum-Product Networks, and Capsule Networks. The attendees leveraged the workshop’s philosophy on interactions, and in various discussions a couple of trends were observed. Particularly, the community agrees on an increasing gap between ML application and hardware capability, with convolutional neural networks as a best-case scenario, as “Beyond-CNN” models will substantially push requirements in terms of structure and computational intensity. In this regard, it is also no surprise that ML and its infrastructure is trending, even though the mileage with existing tooling might vary dramatically.

 

Photos: https://heibox.uni-heidelberg.de/d/41d2d8bb605f43458e7b/