It is our great pleasure to welcome you to the 2016 ACM International Symposium on FPGAs (FPGA 2016). Our mission is to serve as the premier forum for presentation of exciting new research on all aspects of the design and use of Field Programmable Gate Arrays. This includes:
Architecture and circuit design of FPGAs
Computer-aided design algorithms for synthesis, technology mapping, logic and timing optimization, clustering, placement, and routing of FPGAs
High-level abstractions and design tools for FPGA users
FPGA-based and FPGA-like computing engines and accelerators
Innovative FPGA applications and design studies.
In addition, the Symposium is an opportunity for leading FPGA researchers and practitioners from around the world to mingle and share ideas in the relaxed atmosphere of Monterey, California -- convenient to Silicon Valley, yet a world apart.
This year we received 111 submissions -- an increase of 10 per cent -- from 17 countries. The Program Committee accepted 20 full research papers (ten pages), 10 short research papers (six pages), and one tutorial paper, each of which you will find in these proceedings. In addition, 30 other select submissions will be presented as posters at the Symposium; abstracts of these also appear in these proceedings.
This year's evening panel discussion will address the topic "Intel Acquires Altera: How Will the World of FPGAs be Affected?" Bring your tough questions for our expert panelists, concerning either technical or business aspects of this significant change in the FPGA industry landscape. The Symposium kicks off with the co-located Workshop on Overlay Architectures for FPGAs (OLAF). Overlay architectures (e.g. arrays of special-purpose soft processors) are a potentially powerful way to improve design productivity and virtualize FPGAs. Our Designers' Day sessions will be devoted to tutorials for FPGA users.
Cited By
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Bakumenko A, Bakhchevnikov V, Derkachev V, Kovalev A, Lobach V, Potipak M, Valenta C, Shaw J and Kimata M (2020). Crop seed classification based on a real-time convolutional neural network SPIE Future Sensing Technologies, 10.1117/12.2587426, 9781510638617, (98)
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Kang B, Tripathi S, Dane G, Nguyen T and Tescher A (2017). Low-complexity object detection with deep convolutional neural network for embedded systems Applications of Digital Image Processing XL, 10.1117/12.2275512, 9781510612495, (60)
Index Terms
- Proceedings of the 2016 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays