Formulation and Practical Solution for the Optimization of Memory Accesses in Embedded Vision Systems

The design of embedded vision systems, in confronting the "Memory Wall", exhibits many challenges, regarding for example design cost, energy consumption and performance. This paper considers a variant of the Job Shop Scheduling Problem with tooling constraints, arising in this context, in which the completion time (makespan) is to be minimized. This objective corresponds to the performance of the produced circuit.Given a set of tasks and a set of prerequisites, this class of problem aims to schedule all the tasks. Each task can be processed if all its prerequisites (a specific subset of prerequisites) are loaded in the buffers and stay available during its whole operation.We discuss different formulations using integer linear programming and point out its characteristics, namely the size and the quality of the linear programming relaxation bound. To solve this scheduling problem with large size, we compare three sets of approaches including a Constraint Programming, two constructive greedy heuristics (published in previous work), two models of LocalSolver, a Simulated Annealing algorithm and Beam Search algorithm. Numerical experiments are conducted on 16 benchmark instances from the literature as well as on 12 real-life non-linear image processing kernels for validating their efficiency.

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“…[25]. A more detailed description of the proposed model together with a list of all these algorithms can be found in [18] and [19].…”

Section: Background and Motivationmentioning

confidence: 99%

Minimizing makespan under data prefetching constraints for embedded vision systems: a study of optimization methods and their performance

et al. 2021

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“…This problem is discussed quite extensively and many different bad practices or problematic patterns were described in last years. Especially with the incresing interest in the embededd systems, the need for the memory efficient software grows [4].…”

Section: Related Workmentioning

confidence: 99%

Search for the Memory Duplicities in the Java Applications Using Shallow and Deep Object Comparison

Lipka

Potužák

2019

Proceedings of the 2019 Federated Conference on Computer Science and Information Systems

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In high-level object languages, such as Java, a problem of unnecessary duplicates of instances can easily appear. Although there can be a valid reason for maintaining several clones of the same data in the memory, often it indicates that the application can be refactored into a more efficient one. Unnecessary instances consume memory, but in case of Java applications can also have a significant impact on the application performance, as they might prolong the time needed for the garbage collection. In this paper, we are presenting a method and a tool that allows detecting duplicity in the heap dump of a Java application, based on the shallow and deep object comparison. The tool allows to identify the problematic instances in the memory and thus helps programmers to create a better application. On several case studies, we also demonstrate that the duplicates appear not only in the student projects and similar programs that often suffer from poor maintenance but also in commonly available Java tools and frameworks.

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Meeting the Challenges of Optimized Memory Management in Embedded Vision Systems Using Operations Research

Salem

Kieffer

Mancini

2017

Studies in Computational Intelligence

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Formulation and Practical Solution for the Optimization of Memory Accesses in Embedded Vision Systems

Cited by 5 publications

References 14 publications

Minimizing makespan under data prefetching constraints for embedded vision systems: a study of optimization methods and their performance

Minimizing makespan under data prefetching constraints for embedded vision systems: a study of optimization methods and their performance

Search for the Memory Duplicities in the Java Applications Using Shallow and Deep Object Comparison

Meeting the Challenges of Optimized Memory Management in Embedded Vision Systems Using Operations Research

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