On the History of the Growth-Optimal Portfolio

Christensen, Morten

doi:10.1142/9781848168145_0001

Cited by 24 publications

(18 citation statements)

References 193 publications

(244 reference statements)

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“…Those are the Susan edge detector [Smith, 1992] (with an implementation version of Oxford University), KLT feature tracker [Shi and Tomasi, 1994], Fluid simulation [Stam, 2003], the Blowfish application (a symmetric block cipher) from the CHStone benchmark [Hara et al, 2009], AES [SSL, 2012] and Bloom Filter [Christensen, 2012]. 5.…”

Section: Resultsmentioning

confidence: 99%

Hybrid Interconnect Design for Heterogeneous Hardware Accelerators

Pham‐Quoc¹,

Heisswolf²,

Werner³

et al. 2013

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2013

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Heterogeneous multicore systems are becoming increasingly important as the need for computation power grows, especially when we are entering into the big data era. As one of the main trends in heterogeneous multicore, hardware accelerator systems provide application specific hardware circuits and are thus more energy efficient and have higher performance than general purpose processors, while still providing a large degree of flexibility. However, system performance dose not scale when increasing the number of processing cores due to the communication overhead which increases greatly with the increasing number of cores. Although data communication is a primary anticipated bottleneck for system performance, the interconnect design for data communication among the accelerator kernels has not been well addressed in hardware accelerator systems. A simple bus or shared memory is usually used for data communication between the accelerator kernels. In this dissertation, we address the issue of interconnect design for heterogeneous hardware accelerator systems.Evidently, there are dependencies among computations, since data produced by one kernel may be needed by another kernel. Data communication patterns can be specific for each application and could lead to different types of interconnect. In this dissertation, we use detailed data communication profiling to design an optimized hybrid interconnect that provides the most appropriate support for the communication pattern inside an application while keeping the hardware resource usage for the interconnect minimal. Firstly, we propose a heuristicbased approach that takes application data communication profiling into account to design a hardware accelerator system with a custom interconnect. A number of solutions are considered including crossbar-based shared local memory, direct memory access (DMA) supporting parallel processing, local buffers, and hardware duplication. This approach is mainly useful for embedded system where the hardware resources are limited. Secondly, we propose an automated hybrid interconnect design using data communication profiling to define an optimized interconnect for accelerator kernels of a generic hardware accelerator system. The hybrid interconnect consists of a network-on-chip (NoC), vii viii ABSTRACT shared local memory, or both. To minimize hardware resource usage for the hybrid interconnect, we also propose an adaptive mapping algorithm to connect the computing kernels and their local memories to the proposed hybrid interconnect. Thirdly, we propose a hardware accelerator architecture to support streaming image processing. In all presented approaches, we implement the approach using a number of benchmarks on relevant reconfigurable platforms to show their effectiveness. The experimental results show that our approaches not only improve system performance but also reduce overall energy consumption compared to the baseline systems.

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Section: Resultsmentioning

confidence: 99%

Hybrid Interconnect Design for Heterogeneous Hardware Accelerators

Pham‐Quoc¹,

Heisswolf²,

Werner³

et al. 2013

Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE), 2013

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“…As early as 1956, the information theorist John Kelly suggested to optimize long term growth by endogenously adjusting the distribution of types in a population to an exogenously given environmental pattern [33] (for a clear review see [14]). This idea has grown several branches [34], and is known as portfolio theory [35][36][37][38][39][40], growth optimal investment [41][42][43], biological bet-hedging [44], mixed optimal strategies [45], or stochastic (phenotype) switching [46,47]. While Kelly originally worked with the limited case of a diagonal payoff matrix (one type per environmental [47][48][49][50].…”

Section: Portfolio Theory: Optimizing Growthmentioning

confidence: 99%

The More You Know, the More You Can Grow: An Information Theoretic Approach to Growth in the Information Age

Hilbert

2017

Entropy

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In our information age, information alone has become a driver of social growth. Information is the fuel of "big data" companies, and the decision-making compass of policy makers. Can we quantify how much information leads to how much social growth potential? Information theory is used to show that information (in bits) is effectively a quantifiable ingredient of growth. The article presents a single equation that allows both to describe hands-off natural selection of evolving populations and to optimize population fitness in uncertain environments through intervention. The setup analyzes the communication channel between the growing population and its uncertain environment. The role of information in population growth can be thought of as the optimization of information flow over this (more or less) noisy channel. Optimized growth implies that the population absorbs all communicated environmental structure during evolutionary updating (measured by their mutual information). This is achieved by endogenously adjusting the population structure to the exogenous environmental pattern (through bet-hedging/portfolio management). The setup can be applied to decompose the growth of any discrete population in stationary, stochastic environments (economic, cultural, or biological). Two empirical examples from the information economy reveal inherent trade-offs among the involved information quantities during growth optimization.

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“…Indeed, a more risk-seeking attitude than the growth rate maximization (1.1) may not be acceptable for investors, if we recall debates and discussions on risky features of the growth rate optimal portfolio for (1.1). (We can refer to Christensen (2005) and the references therein, for example. )…”

Section: Application Of Cppimentioning

confidence: 99%