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Articles 1 - 3 of 3
Full-Text Articles in Digital Circuits
Achieving A Better Balance Between Productivity And Performance On Fpgas Through Heterogeneous Extensible Multiprocessor Systems, Abazar Sadeghian
Achieving A Better Balance Between Productivity And Performance On Fpgas Through Heterogeneous Extensible Multiprocessor Systems, Abazar Sadeghian
Graduate Theses and Dissertations
Field Programmable Gate Arrays (FPGAs) were first introduced circa 1980, and they held the promise of delivering performance levels associated with customized circuits, but with productivity levels more closely associated with software development. Achieving both performance and productivity objectives has been a long standing challenge problem for the reconfigurable computing community and remains unsolved today. On one hand, Vendor supplied design flows have tended towards achieving the high levels of performance through gate level customization, but at the cost of very low productivity. On the other hand, FPGA densities are following Moore's law and and can now support complete multiprocessor …
Evaluation Of A Field Programmable Gate Array Circuit Reconfiguration System, Jason L. Ives
Evaluation Of A Field Programmable Gate Array Circuit Reconfiguration System, Jason L. Ives
Theses and Dissertations
This research implements a circuit reconfiguration system (CRS) to reconfigure a field programmable gate array (FPGA) in response to a faulty configurable logic block (CLB). It is assumed that the location of the fault is known and the CLB is moved according to one of four replacement methods: column left, column right, row up, and row down. Partial reconfiguration of the FPGA is done through the Joint Test Action Group (JTAG) port to produce the desired logic block movement. The time required to accomplish the reconfiguration is measured for each method in both clear and congested areas of the FPGA. …
Implementation Of Large Neural Networks Using Decomposition, Henry Selvaraj, H. Niewiadomski, P. Buciak, M. Pleban, Piotr Sapiecha, Tadeusz Luba, Venkatesan Muthukumar
Implementation Of Large Neural Networks Using Decomposition, Henry Selvaraj, H. Niewiadomski, P. Buciak, M. Pleban, Piotr Sapiecha, Tadeusz Luba, Venkatesan Muthukumar
Electrical & Computer Engineering Faculty Research
The article presents methods of dealing with huge data in the domain of neural networks. The decomposition of neural networks is introduced and its efficiency is proved by the authors’ experiments. The examinations of the effectiveness of argument reduction in the above filed, are presented. Authors indicate, that decomposition is capable of reducing the size and the complexity of the learned data, and thus it makes the learning process faster or, while dealing with large data, possible. According to the authors experiments, in some cases, argument reduction, makes the learning process harder.