By Manish Verma, Peter Marwedel
This ebook proposes novel reminiscence hierarchies and software program optimization concepts for the optimum usage of reminiscence hierarchies. It offers a variety of optimizations, steadily expanding within the complexity of study and of reminiscence hierarchies. the ultimate bankruptcy covers optimization suggestions for functions inclusive of a number of techniques present in latest embedded devices.
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This ebook proposes novel reminiscence hierarchies and software program optimization innovations for the optimum usage of reminiscence hierarchies. It provides quite a lot of optimizations, gradually expanding within the complexity of research and of reminiscence hierarchies. the ultimate bankruptcy covers optimization ideas for functions together with a number of techniques present in most recent embedded units.
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Additional resources for Advanced Memory Optimization Techniques for Low Power Embedded Processors
Due to the strong inter-dependencies among these subtasks, the code generator uses a genetic algorithm based phase-coupled approach to generate highly optimized code for the M5 DSP. A genetic algorithm is preferred over an Integer Linear Programming (ILP) based approach because of the non-linearity of the optimization problems for the subtasks. Interested readers are referred to  for an in-depth description of the compilation framework. The proposed memory optimizations are integrated into the backend of the compiler for M5 DSP.
9 presents the comparison of normalized energy consumption values when the L1 scratchpad is allocated using the SA approach or the Frac. SA approach. The unit valued baseline represents the energy consumed by the default data memory subsytem of M5 DSP. From the figure, we make a few observations. First, the normalized energy values for the SA approach at 64 bytes (cf.
The profit function EP rof it needs to be maximized in order to minimize the objective function ET otal . The maximization problem resembles the knapsack problem which is known to be an NP-complete problem . 2 (Fractional Scratchpad Allocation (Frac. SA)). Given the set of memory objects M O, the energy model E(mo, mem), and a memory hierarchy consisting of a scratchpad memory (SP M ) and a main memory (M M ). The problem is to determine a subset M OSP M ⊆ M O of the set of memory objects M O such that the full or partial allocation of memory objects moi ∈ M OSP M to the scratchpad memory maximizes the total energy profit EP rof it .
Advanced Memory Optimization Techniques for Low Power Embedded Processors by Manish Verma, Peter Marwedel