Abstract
This paper proposes an approach for optimization of on-chip memory size in data dominated embedded systems. Large amount of array processing is being involved in this category. In order to produce a cost effective system, efficient designing of memory module is quite critical. The memory module configuration being selected by the designer should be well suitable for the application. In this regard, this paper presents a methodology for effective optimization of on-chip memory. For sensitive applications involving large array processing, the entire processing has to be done using embedded modules. While using such module s, care should be taken to meet optimized profile for the design metrics. With help of loop transformation technique, relatively a good amount of memory size requirement is reduced for the arrays. This approach results in a very close memory estimate and an effective optimization. This methodology can be further extended to meet the high level memory optimization applications based on cache characteristics. Speech processing front end mechanism is implemented and shows that this approach gives up to an achievement 61.3% reduction of overall system memory requirement over the estimation approach. Results are provided in terms of comparison of the two approaches of memory estimation and optimization with respect to both of the program and data segments.
Highlights
In today’s embedded systems, memory represents a major bottleneck [1] in terms of cost, performance, and power
To reduce the power consumption, number of off-chip accesses as well as size of storage during memory optimization, loop transformation reordering is presented in this methodology which is much more beneficial
This paper describes a procedure for memory optimization for low power embedded systems
Summary
In today’s embedded systems, memory represents a major bottleneck [1] in terms of cost, performance, and power. A large amount of array processing is being involved in current day embedded applications. It is very critical to come out with methodologies for memory size estimation and optimization. It is important to estimate the memory requirements for the data structures and code segments for that particular application. Memory optimizing transformations are employed to reduce the memory size and number of accesses. This aim at reusing of memory space, giving a fast estimate of memory size. It is preferable to allow sharing among arrays which aids in optimizing the memory size. If sharing is allowed between arrays, the memory size reduces as follows: Struct share {.
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