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... calculate the training error for each application for every footprint as a percentage difference between the actual performance (F max ) and the predicted performance from the model. The average training error across all applications at every footprint for each device is shown in Table 3. Testing the model To evaluate the effectiveness of the model, we use three applications, where the expected output (ground truth) was first obtained by compiling these applications using Quartus at every footprint. ...Similar publications
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Este documento presenta y describe los avances alcanzados y tareas realizadas hasta el momento en el PIDDEF 03/ESP/15 BAA (Proyecto de Investigación y Desarrollo de interés para la Defensa) que propicia el diseño y desarrollo de un transmisor de aplicaciones telemétricas en la Banda de frecuencias S (2.1- 2.3 GHz).
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Citations
The increasing size of modern FPGAs allows for ever more complex applications to be mapped onto them. However, long design implementation times for large designs can severely affect design productivity. A modular design methodology can improve design productivity in a divide and conqueror fashion but at the expense of degraded performance and power consumption of the resulting implementation. To reduce the dominant power dissipation component in FPGAs, the routing power, methodologies have been proposed that consider data communication between modules during module formation and placement on the FPGA. Selecting proper mapping region on target FPGAs, on the other hand, is becoming a critical process because of the heterogeneous resources and column arrangements in modern FPGAs. Selecting inappropriate FPGA regions for mapping could lead to degraded performance. Hence, we propose a methodology that uses communication-aware module placement, such that modules are mapped by selecting the best shape and region on the FPGA factoring the columnar resource arrangements. Additionally, techniques for module locking and splitting have been proposed for deterministic convergence of the algorithm and for improved module placement. This methodology exhibits nearly 19% routing power reduction with respect to commercial CAD flows without any degradation in achievable performance.
