Modern embedded systems are rapidly increasing their complexity, both in terms of numbers of cores, as well as heterogeneity. To generate efficient code for these systems, it is common to leverage formal models of computation. Among these, the dataflow model of Kahn Process Networks (KPN) is widespread because it is expressive but guarantees a deterministic execution. However, the KPN model is ill-suited to expose data-level parallelism, since this has to be made explicit in the process network. This is aggravated by the fact that its most common execution model, Kahn-MacQueen, poses restrictive conditions on the scheduling of data-parallel processes, leading to an inefficient execution. In this paper we present a novel extension to the KPN model and a relaxed execution strategy that addresses this problem, while keeping the deterministic KPN semantics. It improves run-time adaptivity in malleable way and provides implicit parallelism. We evaluate our approach on two architectures, improving the performance of a benchmark by up to 25.6 % on an Intel chip with hyper-threading, and by up to 78.0 % on a heterogeneous embedded ARM big.LITTLE architecture.