59837401
An Analytical Approach to Generation and Exploration of Reconfigurable Architectures
537185
A Novel Hueristic and Provable Bounds for Reconfigurable Architecture Design
This paper is concerned with the application of formal optimisation methods to the design of mixed-granularity FPGAs. In particular, the authors investigate the appropriate mix and floorplan of heterogeneous elements: multipliers, RAMs, and LUT-based logic, in order to maximise the performance of a set of DSP benchmark applications, given a fixed silicon budget. The authors extend our previous mathematical programming framework by proposing a novel set of heuristics, capable of providing upper bounds on the achievable reconfigurable-to-fixed-logic performance ratio. The results provide, for the first time, quantifications of the optimal performance/area-enhancing capability of multipliers and RAM blocks within a system context, and indicate that only a minimal performance benefit can be achieved over Virtex II by re-organising the device floorplan, when using optimal technology mapping
2002086
Exploration of heterogeneous reconfigurable architectures
In contrast to early reconfigurable hardware, modern reconfigurable devices consist of a mixture of heterogeneous components. The heterogeneous components introduced in recent devices are both line and coarse-grain in their nature, and are primarily aimed at improving the speed, power consumption and area metrics of target designs. The aim of this PhD project is to look at analytical ways of exploring the design space of modern reconfigurable hardware. By automatically generating architectures and estimating benchmark performance, linear programming can eliminate architectural possibilities in an efficient way, meaning that a large number of different architectures can be examined in a fast and optimal manner.
12877131
An Australasian Experience of the use of Selected Technologies in the Delivery of a Legal Education Program - Some Lessons for Faculties and Educational Program Planners
14158820
Disk caching in large database and timeshared systems
We present the results of a variety of trace-driven simulations of disk cache designs using traces from a variety of mainframe timesharing and database systems in production use. We compute miss ratios, run lengths, traffic ratios, cache residency times, degree of memory pollution and other statistics for a variety of designs, varying lock size, prefetching algorithm and write algorithm. We find that for this workload, sequential prefetching produces a significant (about 20%) but still limited improvement in the miss ratio, even using a powerful technique for detecting sequentiality. Copy-back writing decreased write traffic relative to write-through by more than 50%; periodic flushing of the dirty blocks increased write traffic only slightly compared to pure write-back, and then only for large cache sizes. Write-allocate had little effect compared to no-write-allocate. Block sizes of over a track don't appear to be useful. Limiting cache occupancy by a single process or transaction appears to have little effect. This study is unique in the variety and quality of the data used in the studies.
13460881
Multi-objective tabu search using a multinomial probability mass function
A tabu search approach to solve multi-objective combinatorial optimization problems is developed in this paper. This procedure selects an objective to become active for a given iteration with a multinomial probability mass function. The selection step eliminates two major problems of simple multi-objective methods, a priori weighting and scaling of objectives. Comparison of results on an NP-hard combinatorial problem with a previously published multi-objective tabu search approach and with a deterministic version of this approach shows that the multinomial approach is effective, tractable and flexible.
6414828
Local Search Genetic Algorithm for Optimization of Highly Reliable Communications Networks
This paper presents a genetic algorithm (GA) with specialized encoding, initialization and local search genetic operators to optimize communication network topologies. This NPhard problem is often highly constrained so that random initialization and standard genetic operators usually generate infeasible network architectures. Compounding this infeasibility issue is that the fitness function involves calculating the all-terminal reliability of the network, a calculation which is computationally expensive. Therefore, it is imperative that the search balances the need to thoroughly explore the boundary between feasible and infeasible networks, along with calculating fitness on only the most promising candidate networks. The algorithm results are compared to optimum results found by branch and bound and also to GA results without local search operators on a suite of 79 test problems. This GA strategy of employing bounds, simple heuristic checks and problem specific repair and local search operators can be used on other highly constrained combinatorial applications where numerous fitness calculations are prohibitive.
16194916
Cross-Situational Learning: A Mathematical Approach
We present a mathematical model of cross-situational learning, in which we quantify the learnability of words and vocabularies. We find that high levels of uncertainty are not an impediment to learning single words or whole vocabulary systems, as long as the level of uncertainty is somewhat lower than the total number of meanings in the system. We further note that even large vocabularies are learnable through cross-situational learning.