A Multi-Functional unit has several functions and these can be changed with a control signal. For High-Level Synthesis, using Multi-Functions units in operation chaining make it possible to obtaining the solution with the same number of control steps and less resources compared to that without them. This paper proposes an operation chaining method considering Multi-Functional units. The method formulates module selection, scheduling, and functional unit allocation with operation chaining as a 0/1 integer linear problem and obtains optimal solution with minimum number of control steps under area and clock-cycle type constraints. The first contribution of this paper is to propose the global search for operation chaining with Multi-Functional units having multiple outputs as well as with single output. The second contribution is to condier the area constraint as a resource constraint instead of the type and number of functional units. Experimental results show that chaining with Multi-Functional units is effective and the proposed method is useful to evaluate heuristic algorithms.

This paper proposes a novel Behavioral Synthesis method that tries to reduce the number of clock cycles under clock cycle time and total functional unit area constraints using special functional units efficiently. Special functional units are designed to have shorter delay and/or smaller area than the cascaded basic functional units for specific operation patterns. For example, a Multiply-Accumulator is one of them. However, special functional units may have less flexibility for resource sharing because intermediate operation results may not be able to be obtained. Hence, almost all conventional methods can not handle special functional units efficiently for the reduction of clock cycles in practical time, especially under a tight area constraint. The proposed method makes it possible to solve module selection, scheduling, and functional unit allocation problems using special functional units in practical time with some heuristics. Experimental results show that the proposed method has achieved maximally 33% reduction of the cycles for a small application and 14% reduction for a realistic application in practical time.