English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a heuristic algorithm for the run-time distribution of task sets in a homogeneous Multiprocessornetwork-on-chip. The algorithm is itself distributed over the processors and thus can be applied to systems of arbitrary size. Also, tasks added at run-time can be handled without any difficulty, allowing for inline optimisation. Based on local information on processor workload, task size, communication requirements, and link contention, iterative decisions on task migrations to other processors are made. The mapping results for several example tasksets are first compared with those of an exact (enumeration) algorithm with global information for a 3×3 processor array. The results show that the mapping quality achieved by our distributed algorithm is  within 25%of that of the exact algorithm. For larger array sizes, simulated annealing is used as a reference and the behaviour of our algorithm is investigated. The mapping quality of the algorithm can be shown to be within a reasonable range (below 30% mostly) of the reference. This adaptability and the low computation and communication overhead of the distributed heuristic clearly indicate that decentralised algorithms are a favourable solution for anautomatic task distribution

A Decentralised Task Mapping Approach for Homogeneous Multiprocessor Network-On-Chips