By Manish Verma, Peter Marwedel
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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.
13 µm technology. Though the energy model is not as detailed as the previous measurement based instruction level energy model, it is sufficiently accurate for a simple ARM7 processor. 13 µm technology. In addition, the framework includes energy models for the ST-Bus also obtained from STMicroelectronics. However, no energy model is included for the AMBA-Bus. A detailed discussion on the energy models for the multi-processor simulation framework can be found in . Fig. 6. 2 Compilation Framework The compilation framework for the multi-processor ARM based systems includes a source level memory optimizer which is based on the ICD-C compilation framework  and GCC’s cross compiler tool chain for ARM processors.
This is because the larger the scratchpad, the more memory objects are allocated and the less are the accesses to the slow and energy inefficient main memory. On a closer look, it is observed that the energy and execution values decrease in a stepwise manner. 4(b), the energy consumption values remain the same for 100 bytes and 128 bytes scratchpad. This behavior emerges due to granularity of the SA approach. For uni-processor ARM setup, the SA approach allocates traces, functions and global variables to the scratchpad.
Advanced Memory Optimization Techniques for Low Power Embedded Processors by Manish Verma, Peter Marwedel