Fast prototyping and refinement of complex dynamic data types in multimedia applications for consumer embedded devices

Abstract

Abstract-Portable consumer devices are increasing more and more their capabilities and can now implement new multimedia algorithms that were resewed only for powerful workstations few years ago. Unfortunately, the original design characteristics of such algorithms do not often allow to port them directly to current embedded devices. These algorithms share complex and intensive dynamic memory use and actual embedded systems cannot provide efficient general-purpose memory management as it is needed. As a result, dynamic memory optimizations are a requirement when porting these applications. Within these optimizations, the refinement of the dynamically (de)allocated abstract data type implementations in the complex multimedia applications involved is one of the most important and difficult parts for an efficient mapping of the algorithms on low-power and high-speed embedded consumer devices. In this paper, we describe a high-level approach for modeling and refining complex data types wing abstract derived classes in C++. This approach enables the multimedia developer to compose, evaluate and refine complex data types in a conceptually straightforward way, without a time-consuming programming effort.I. INTRODUCTION Multimedia applications have experienced recently a very fast growth in their variety, complexity and functionality. This implies a high demand of memory and performance, which results in high cost and power consumption systems. These new algorithms (e.g. MPEG4) depend, with few exceptions, on Dynamic Memory (DM from now on) for their operations due to the inherent unpredictability of the input data, which heavily influences global performance and memory usage of these systems. In addition, energy has become a real issue in overall system design (both embedded and general-purpose) due to circuit reliability and packaging costs [12]. Thus, optimizations include three goals that cannot he seen independently: memory usage, power consumptions and performance.Since the DM subsystem heavily influences performance and is a very important source of power consumption and memory usage, system-level exploration mechanisms must he available at an early stage of the design flow for embedded systems. Unfortunately, general approaches do not exist presently at this level for the dynamic implementations of the Abstract Data Types (ADTs) involved, which are sets of data values and associated operations that are specified independently of any particular implementation [4]. This definition stresses more on the effects of operations than the language-specific implementation of the ADTs (or data type implementation).In the following, we will focus on the implementation of these dynamically (de)allocated abstract data types (or DDTs for short from now on). When DDTs are used in programs, they can be implemented by the developer in the most naive form (because the developer is more focussed on the algorithm itself) or in a manually optimized implementation where the number of implementation alternatives is defined by the experi...

Optimization methodology of dynamic data structures based on genetic algorithms for multimedia embedded systems

“…The implementation of a DDT has two main components: First, it has storage aspects that determine how data memory is allocated and freed at run-time and how this memory is tracked; Second, it includes an access component, which can refer to two different basic access patterns: sequential or iterator-based and random access. In our case, after studying current state of the art multimedia applications like 3D video games and physics engines, we have classified the DDT implementations in basic and multi-layer implementations relevant for embedded multimedia applications, as proposed in Daylight et al (2004), and Atienza et al (2004). Trees, hash tables, graphs, all utilize these structures and build upon them a more complex access pattern.…”

“…2008.08.032 experiments to be carried out typically becomes unaffordable even for a small number of DDTs. For instance, in the case of an embedded application including 10 different DDTs that need to be explored for 10 basic relevant implementations of DDTs for multimedia applications (as proposed in Atienza et al (2004), Bartzas et al (2006), Leeman (2003)), the number of experiments (i.e. multiple runs of the application) that need to be performed is 10 10 ; testing all these combinations manually is not feasible.…”