More FLOPS or more precision? Accuracy Parameterizable Linear Equation Solvers for Model-Predictive Control

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• Antonio Roldao (PhD@IC)
• Thursday 05 February 2009, 12:00-13:00
• Mahanakorn Laboratory, EEE.

If you have a question about this talk, please contact Dr George A Constantinides.

In this work we exploit FPGA flexibility in the context of accelerating the solution of many small systems of linear equations, a problem central to model predictive control (MPC). Using iterative methods for solving these systems, one can obtain an improved accuracy either by running for more iterations or by using more precise internal computations, unlike direct methods, where accuracy is only a function of operation precision. Thus, in iterative methods, for a given accuracy requirement we may conduct fewer iterations in a higher precision, or more in a lower precision. We argue that this suits FPGA architectures ideally, as low precision operations result in greater parallelism for any fixed area constraint. We show that we may therefore optimize the performance by balancing iteration count and operation precision, resulting in a several-fold speed improvement over a double-precision implementation, but with the same final result accuracy. Exploring this trade-off it is possible to provide a speed-up of 23 times on average, 10 on the worst case and 38 on the best, compared to a high-end CPU running at 3.0 GHz. This has the potential to allow modern high-performance control techniques to be used in novel settings such as aircraft and diesel engines.

Joint work with Amir Shahzad, George Constantinides, and Eric Kerrigan

This talk is part of the CAS FPGA Talks series.

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• CAS FPGA Talks
• Mahanakorn Laboratory, EEE

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