By Manish Verma, Peter Marwedel
This booklet proposes novel reminiscence hierarchies and software program optimization thoughts for the optimum usage of reminiscence hierarchies. It offers quite a lot of optimizations, gradually expanding within the complexity of research and of reminiscence hierarchies. the ultimate bankruptcy covers optimization concepts for functions together with a number of strategies present in newest embedded devices.
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Additional info for Advanced Memory Optimization Techniques for Low Power Embedded Processors
The execution count for a function is the sum of the execution counts of every instruction in the function, whereas the access count for an array is the sum of the access counts of each array element. 1. First, the total instruction size is much smaller than the total data size of the application, while the total execution count for instructions is an order of magnitude larger than the total access count for data arrays. This implies that instructions should belong to the set of memory objects as they have high execution count and consume much less space.
After the code selection step, the control flow graph (CFG) which represents basic blocks as nodes and the possible execution flow as edges, is generated. The control flow graph is then optimized using standard processor dependent optimizations, like register allocation1 , instruction scheduling and peephole optimization. The backend optimizer also includes a well known instruction cache optimization called trace generation . The instruction cache optimization provides the foundation for the memory optimizations proposed in the subsequent chapters and therefore, it is described seperately in the following subsection.
In the following section, we present the ILP based SA approach and the greedy algorithm based Frac. SA approach. 5 Non-Overlayed Scratchpad Allocation The current section presents an integer linear programming (ILP) based optimal approach to solve the SA problem and a greedy algorithm based fractional scratchpad allocation approach. In the following, we start by presenting the ILP formulation of the SA problem. 1 Optimal Non-Overlayed Scratchpad Allocation Let us define the following binary variable l(moi ) to denote the location of the memory object moi in the memory hierarchy.
Advanced Memory Optimization Techniques for Low Power Embedded Processors by Manish Verma, Peter Marwedel