15,589 research outputs found
A Quantum Langevin Formulation of Risk-Sensitive Optimal Control
In this paper we formulate a risk-sensitive optimal control problem for
continuously monitored open quantum systems modelled by quantum Langevin
equations. The optimal controller is expressed in terms of a modified
conditional state, which we call a risk-sensitive state, that represents
measurement knowledge tempered by the control purpose. One of the two
components of the optimal controller is dynamic, a filter that computes the
risk-sensitive state.
The second component is an optimal control feedback function that is found by
solving the dynamic programming equation. The optimal controller can be
implemented using classical electronics.
The ideas are illustrated using an example of feedback control of a two-level
atom
A metadata-enhanced framework for high performance visual effects
This thesis is devoted to reducing the interactive latency of image processing computations in
visual effects. Film and television graphic artists depend upon low-latency feedback to receive
a visual response to changes in effect parameters. We tackle latency with a domain-specific optimising
compiler which leverages high-level program metadata to guide key computational and
memory hierarchy optimisations. This metadata encodes static and dynamic information about
data dependence and patterns of memory access in the algorithms constituting a visual effect –
features that are typically difficult to extract through program analysis – and presents it to the
compiler in an explicit form. By using domain-specific information as a substitute for program
analysis, our compiler is able to target a set of complex source-level optimisations that a vendor
compiler does not attempt, before passing the optimised source to the vendor compiler for
lower-level optimisation.
Three key metadata-supported optimisations are presented. The first is an adaptation of
space and schedule optimisation – based upon well-known compositions of the loop fusion and
array contraction transformations – to the dynamic working sets and schedules of a runtimeparameterised
visual effect. This adaptation sidesteps the costly solution of runtime code generation
by specialising static parameters in an offline process and exploiting dynamic metadata to
adapt the schedule and contracted working sets at runtime to user-tunable parameters. The second
optimisation comprises a set of transformations to generate SIMD ISA-augmented source code.
Our approach differs from autovectorisation by using static metadata to identify parallelism, in
place of data dependence analysis, and runtime metadata to tune the data layout to user-tunable
parameters for optimal aligned memory access. The third optimisation comprises a related set
of transformations to generate code for SIMT architectures, such as GPUs. Static dependence
metadata is exploited to guide large-scale parallelisation for tens of thousands of in-flight threads.
Optimal use of the alignment-sensitive, explicitly managed memory hierarchy is achieved by identifying
inter-thread and intra-core data sharing opportunities in memory access metadata.
A detailed performance analysis of these optimisations is presented for two industrially developed
visual effects. In our evaluation we demonstrate up to 8.1x speed-ups on Intel and AMD
multicore CPUs and up to 6.6x speed-ups on NVIDIA GPUs over our best hand-written implementations
of these two effects. Programmability is enhanced by automating the generation of
SIMD and SIMT implementations from a single programmer-managed scalar representation
Efficient Linear Programming for Dense CRFs
The fully connected conditional random field (CRF) with Gaussian pairwise
potentials has proven popular and effective for multi-class semantic
segmentation. While the energy of a dense CRF can be minimized accurately using
a linear programming (LP) relaxation, the state-of-the-art algorithm is too
slow to be useful in practice. To alleviate this deficiency, we introduce an
efficient LP minimization algorithm for dense CRFs. To this end, we develop a
proximal minimization framework, where the dual of each proximal problem is
optimized via block coordinate descent. We show that each block of variables
can be efficiently optimized. Specifically, for one block, the problem
decomposes into significantly smaller subproblems, each of which is defined
over a single pixel. For the other block, the problem is optimized via
conditional gradient descent. This has two advantages: 1) the conditional
gradient can be computed in a time linear in the number of pixels and labels;
and 2) the optimal step size can be computed analytically. Our experiments on
standard datasets provide compelling evidence that our approach outperforms all
existing baselines including the previous LP based approach for dense CRFs.Comment: 24 pages, 10 figures and 4 table
NATURAL ALGORITHMS IN DIGITAL FILTER DESIGN
Digital filters are an important part of Digital Signal Processing (DSP), which plays
vital roles within the modern world, but their design is a complex task requiring a great
deal of specialised knowledge. An analysis of this design process is presented, which
identifies opportunities for the application of optimisation.
The Genetic Algorithm (GA) and Simulated Annealing are problem-independent
and increasingly popular optimisation techniques. They do not require detailed prior
knowledge of the nature of a problem, and are unaffected by a discontinuous search
space, unlike traditional methods such as calculus and hill-climbing.
Potential applications of these techniques to the filter design process are discussed,
and presented with practical results. Investigations into the design of Frequency Sampling
(FS) Finite Impulse Response (FIR) filters using a hybrid GA/hill-climber proved
especially successful, improving on published results. An analysis of the search space
for FS filters provided useful information on the performance of the optimisation technique.
The ability of the GA to trade off a filter's performance with respect to several design
criteria simultaneously, without intervention by the designer, is also investigated.
Methods of simplifying the design process by using this technique are presented, together
with an analysis of the difficulty of the non-linear FIR filter design problem from
a GA perspective. This gave an insight into the fundamental nature of the optimisation
problem, and also suggested future improvements.
The results gained from these investigations allowed the framework for a potential
'intelligent' filter design system to be proposed, in which embedded expert knowledge,
Artificial Intelligence techniques and traditional design methods work together. This
could deliver a single tool capable of designing a wide range of filters with minimal
human intervention, and of proposing solutions to incomplete problems. It could also
provide the basis for the development of tools for other areas of DSP system design
A Domain Specific Approach to High Performance Heterogeneous Computing
Users of heterogeneous computing systems face two problems: firstly, in
understanding the trade-off relationships between the observable
characteristics of their applications, such as latency and quality of the
result, and secondly, how to exploit knowledge of these characteristics to
allocate work to distributed computing platforms efficiently. A domain specific
approach addresses both of these problems. By considering a subset of
operations or functions, models of the observable characteristics or domain
metrics may be formulated in advance, and populated at run-time for task
instances. These metric models can then be used to express the allocation of
work as a constrained integer program, which can be solved using heuristics,
machine learning or Mixed Integer Linear Programming (MILP) frameworks. These
claims are illustrated using the example domain of derivatives pricing in
computational finance, with the domain metrics of workload latency or makespan
and pricing accuracy. For a large, varied workload of 128 Black-Scholes and
Heston model-based option pricing tasks, running upon a diverse array of 16
Multicore CPUs, GPUs and FPGAs platforms, predictions made by models of both
the makespan and accuracy are generally within 10% of the run-time performance.
When these models are used as inputs to machine learning and MILP-based
workload allocation approaches, a latency improvement of up to 24 and 270 times
over the heuristic approach is seen.Comment: 14 pages, preprint draft, minor revisio
Sparse Filter Design Under a Quadratic Constraint: Low-Complexity Algorithms
This paper considers three problems in sparse filter design, the first involving a weighted least-squares constraint on the frequency response, the second a constraint on mean squared error in estimation, and the third a constraint on signal-to-noise ratio in detection. The three problems are unified under a single framework based on sparsity maximization under a quadratic performance constraint. Efficient and exact solutions are developed for specific cases in which the matrix in the quadratic constraint is diagonal, block-diagonal, banded, or has low condition number. For the more difficult general case, a low-complexity algorithm based on backward greedy selection is described with emphasis on its efficient implementation. Examples in wireless channel equalization and minimum-variance distortionless-response beamforming show that the backward selection algorithm yields optimally sparse designs in many instances while also highlighting the benefits of sparse design.Texas Instruments Leadership University Consortium Progra
A discrete invitation to quantum filtering and feedback control
The engineering and control of devices at the quantum-mechanical level--such
as those consisting of small numbers of atoms and photons--is a delicate
business. The fundamental uncertainty that is inherently present at this scale
manifests itself in the unavoidable presence of noise, making this a novel
field of application for stochastic estimation and control theory. In this
expository paper we demonstrate estimation and feedback control of quantum
mechanical systems in what is essentially a noncommutative version of the
binomial model that is popular in mathematical finance. The model is extremely
rich and allows a full development of the theory, while remaining completely
within the setting of finite-dimensional Hilbert spaces (thus avoiding the
technical complications of the continuous theory). We introduce discretized
models of an atom in interaction with the electromagnetic field, obtain
filtering equations for photon counting and homodyne detection, and solve a
stochastic control problem using dynamic programming and Lyapunov function
methods.Comment: 76 pages, 12 figures. A PDF file with high resolution figures can be
found at http://minty.caltech.edu/papers.ph
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