Parallel history sensitive computations in dataflow architecture

This paper addresses the history sensitive computation problem in dataflow architecture. Current solutions use memory which is going a step backwards to Von Neumann model of computers. Furthermore, these solutions are not suitable for exploiting parallelism. In this paper, a new memoryless solution is reviewed and approaches based on forwarding, pipelining, and loop unfolding to maximize its parallel processing are presented. Forwarding mechanisms are presented for both static and dynamic dataflow environments.