Code Optimization Techniques for Embedded Processors: by Rainer Leupers

By Rainer Leupers

The construction blocks of modern-day and destiny embedded structures are complicated highbrow estate elements, or cores, lots of that are programmable processors. regularly, those embedded processors normally were seasoned­ grammed in meeting languages as a result of potency purposes. this suggests time­ eating programming, wide debugging, and occasional code portability. the necessities of brief time-to-market and dependability of embedded platforms are patently far better met by utilizing high-level language (e.g. C) compil­ ers rather than meeting. besides the fact that, using C compilers usually incurs a code caliber overhead compared to manually written meeting courses. as a result of the desire for effective embedded structures, this overhead has to be very low with the intention to make compilers necessary in perform. In flip, this calls for new compiler strategies that take the categorical constraints in embedded method de­ signal into consideration. An instance are the really expert architectures of modern DSP and multimedia processors, which aren't but sufficiently exploited by means of present compilers.

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Therefore, retargeting to domain-specific architectures is very difficult and the generated compilers have problems with special-purpose registers, instruction-level parallelism, and the memory access structure. This is also confirmed by the DSPStone project [ZVSM94], where several GNU-based C compilers for DSPs turned out to generate code of very low quality. 5), several research projects aimed at providing retargetable compiler technology for embedded processors. The MSSQ compiler [Nowa87, Marw93, LeMa98] is capable of generating microcode for almost arbitrary data path architectures modeled in an HDL.

As in most programming languages, C has the concept of functions and local variables. These variables exist only during the execution of some particular function at program runtime and therefore have to reside in a runtime stack or, if possible, in registers. For DSPs, which have a very limited amount of registers, it is reasonable to assume that in fact most local variables have to reside in the stack. ,. 3. a) Stack layout during function execution: The stack contains the function parameters (pushed by the calling function), the return address (pushed by the call instruction), the local variables, and a spill space for temporarily saving register contents.

2 This is sometimes called a roving or floating frame pointer. 5. maximum weighted path b) 35 a d b offset assignment c) Access graph model for offset assignment 2. e. a path that touches all nodes once. From these observations it is obvious that an optimum offset assignment corresponds to a maximum weighted Hamiltonian path in G (fig. 5 band c). Bartley presented an 0( n 3 ) algorithm that heuristically finds good paths. In [LDK+95b], Liao showed that the offset assignment problem is NP-hard even for the simple case (1, 0, 1) -OA, and he presented a more efficient heuristic also based on the access graph model.

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