@article{Longo:2014:10.1016/j.automatica.2013.09.035,
author = {Longo, S and Kerrigan, EC and Constantinides, GA},
doi = {10.1016/j.automatica.2013.09.035},
journal = {Automatica},
pages = {162--168},
title = {Constrained LQR for low-precision data representation},
url = {http://dx.doi.org/10.1016/j.automatica.2013.09.035},
volume = {50},
year = {2014}
}

Download

TY  - JOUR
AB  - Performing computations with a low-bit number representation results in a faster implementation that uses less silicon, and hence allows an algorithm to be implemented in smaller and cheaper processors without loss of performance. We propose a novel formulation to efficiently exploit the low (or non-standard) precision number representation of some computer architectures when computing the solution to constrained LQR problems, such as those that arise in predictive control. The main idea is to include suitably-defined decision variables in the quadratic program, in addition to the states and the inputs, to allow for smaller roundoff errors in the solver. This enables one to trade off the number of bits used for data representation against speed and/or hardware resources, so that smaller numerical errors can be achieved for the same number of bits (same silicon area). Because of data dependencies, the algorithm complexity, in terms of computation time and hardware resources, does not necessarily increase despite the larger number of decision variables. Examples show that a 10-fold reduction in hardware resources is possible compared to using double precision floating point, without loss of closed-loop performance.
AU  - Longo,S
AU  - Kerrigan,EC
AU  - Constantinides,GA
DO  - 10.1016/j.automatica.2013.09.035
EP  - 168
PY  - 2014///
SN  - 1873-2836
SP  - 162
TI  - Constrained LQR for low-precision data representation
T2  - Automatica
UR  - http://dx.doi.org/10.1016/j.automatica.2013.09.035
UR  - http://hdl.handle.net/10044/1/31109
VL  - 50
ER  - 

Download