8,084 research outputs found
Optimization of Discrete-parameter Multiprocessor Systems using a Novel Ergodic Interpolation Technique
Modern multi-core systems have a large number of design parameters, most of
which are discrete-valued, and this number is likely to keep increasing as chip
complexity rises. Further, the accurate evaluation of a potential design choice
is computationally expensive because it requires detailed cycle-accurate system
simulation. If the discrete parameter space can be embedded into a larger
continuous parameter space, then continuous space techniques can, in principle,
be applied to the system optimization problem. Such continuous space techniques
often scale well with the number of parameters.
We propose a novel technique for embedding the discrete parameter space into
an extended continuous space so that continuous space techniques can be applied
to the embedded problem using cycle accurate simulation for evaluating the
objective function. This embedding is implemented using simulation-based
ergodic interpolation, which, unlike spatial interpolation, produces the
interpolated value within a single simulation run irrespective of the number of
parameters. We have implemented this interpolation scheme in a cycle-based
system simulator. In a characterization study, we observe that the interpolated
performance curves are continuous, piece-wise smooth, and have low statistical
error. We use the ergodic interpolation-based approach to solve a large
multi-core design optimization problem with 31 design parameters. Our results
indicate that continuous space optimization using ergodic interpolation-based
embedding can be a viable approach for large multi-core design optimization
problems.Comment: A short version of this paper will be published in the proceedings of
IEEE MASCOTS 2015 conferenc
Constructing multiple unique input/output sequences using metaheuristic optimisation techniques
Multiple unique input/output sequences (UIOs) are often used to generate robust and compact test sequences in finite state machine (FSM) based testing. However, computing UIOs is NP-hard. Metaheuristic optimisation techniques (MOTs) such as genetic algorithms (GAs) and simulated annealing (SA) are effective in providing good solutions for some NP-hard problems. In the paper, the authors investigate the construction of UIOs by using MOTs. They define a fitness function to guide the search for potential UIOs and use sharing techniques to encourage MOTs to locate UIOs that are calculated as local optima in a search domain. They also compare the performance of GA and SA for UIO construction. Experimental results suggest that, after using a sharing technique, both GA and SA can find a majority of UIOs from the models under test
Seeking Quantum Speedup Through Spin Glasses: The Good, the Bad, and the Ugly
There has been considerable progress in the design and construction of
quantum annealing devices. However, a conclusive detection of quantum speedup
over traditional silicon-based machines remains elusive, despite multiple
careful studies. In this work we outline strategies to design hard tunable
benchmark instances based on insights from the study of spin glasses - the
archetypal random benchmark problem for novel algorithms and optimization
devices. We propose to complement head-to-head scaling studies that compare
quantum annealing machines to state-of-the-art classical codes with an approach
that compares the performance of different algorithms and/or computing
architectures on different classes of computationally hard tunable spin-glass
instances. The advantage of such an approach lies in having to only compare the
performance hit felt by a given algorithm and/or architecture when the instance
complexity is increased. Furthermore, we propose a methodology that might not
directly translate into the detection of quantum speedup, but might elucidate
whether quantum annealing has a "`quantum advantage" over corresponding
classical algorithms like simulated annealing. Our results on a 496 qubit
D-Wave Two quantum annealing device are compared to recently-used
state-of-the-art thermal simulated annealing codes.Comment: 14 pages, 8 figures, 3 tables, way too many reference
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