High-level synthesis using hierarchical conditional dependency graphs in the CODESIS system

In previous work in behavioral high-level synthesis (HLS), data-flow and control-flow dominated descriptions are treated separately. A result of such a separation is that efficient techniques have been developed for the HLS of data-flow dominated behavioral descriptions. However, HLS of control-flow dominated descriptions still lags behind. To close this gap in this paper we propose a complete HLS framework based on an internal design representation where control- and data-flows are uniformly represented and disposes a formal foundation. Based on it conditional behaviors can be efficiently scheduled combining conditional resource sharing (CSR), speculative execution (SE) and other formal graph transformations. These techniques and tools covering all HLS activities have been organized in the CODESIS tool destined for both research and educational purposes.