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2019 2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)  
Compete When You Can Work Together: FPGA-ASIC Integration for Persistent RNNs 199 Eriko Nurvitadhi (Intel Corporation), Dongup Kwon (Intel Corporation), Ali Jafari (Intel Corporation), Andrew Boutros  ...  Leong (The University of Sydney) Towards Efficient Deep Neural Network Training by FPGA-Based Batch-Level Parallelism RapidRoute: Fast Assembly of Communication Structures for FPGA Overlays 61 Leo Liu  ... 
doi:10.1109/fccm.2019.00004 fatcat:qku57w2j2vfs3kluykjmqfbzya

Rebooting Neuromorphic Hardware Design – A Complexity Engineering Approach [article]

Natesh Ganesh
2020 arXiv   pre-print
The time is ideal for a significant reboot of our design methodologies and success will represent a radical shift in how neuromorphic hardware is designed and pave the way for a new paradigm.  ...  As the compute demands for machine learning and artificial intelligence applications continue to grow, neuromorphic hardware has been touted as a potential solution.  ...  FPGAs, ASICs and system on a chip (SoC) for parallel processing, scientific computing, high performance computing, graphic processing units, etc are perfect examples of modifying (c) the system architecture  ... 
arXiv:2005.00522v2 fatcat:66m4bc2dyjf7fhqzfczwwxva4e

Proceedings of the 2020 Connecting the Dots Workshop

David Lange
2020 Zenodo  
Complete set of all proceedings contributed and reviewed for the 2020 Connecting the Dots workshop.  ...  The Connecting The Dots workshop series brings together experts on track reconstruction and other problems involving pattern recognition in sparsely sampled data.  ...  Acknowledgements The authors would like to extend a heartfelt thank you to Andreas Hoecker for his support.  ... 
doi:10.5281/zenodo.4088760 fatcat:kulhvq3t5fglnimqawge7lnt4q