AbstractIn this article, parallel computation of manipulator inverse dynamics is investigated. A hierarchical graph‐based mapping approach is devised to analyze the inherent parallelism in the Newton‐Euler formulation at several computational levels, and to derive the features of an abstract architecture for exploitation of parallelism. At each level, a parallel algorithm represents the application of a parallel model of computation that transforms the computation into a graph whose structure defines the features of an abstract architecture, i.e., number of processors, communication structure, etc. Data flow analysis is employed to derive the time lower bound in the computation as well as the sequencing of the abstract architecture. The features of the target architecture are defined by optimization of the abstract architecture to exploit maximum parallelism while minimizing various overheads and architectural complexity. An algorithmically specialized, highly parallel, MIMD‐SIMD architecture is designed and implemented that is capable of efficient exploitation of parallelism at several computational levels. The computation time of the Newton‐Euler formulation for a 6‐degree‐of‐freedom (dof) general manipulator is measured as 187 μs. The increase in computation time for each additional dof is 23 μs, which leads to a computation time of less than 500 μs, even for a 12
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