This paper presents a tool for exploring different parallelization options for an application. It can be used to quickly find a high-quality match between an application and a multi-processor platform architecture. By specifying the parallelization at a high abstraction level, and leaving the actual source code transformations to the tool, a designer can try out many parallelizations in a short time. A parallelization may use either functional or data-level splits, or a combination of both. An accompanying high-level simulator provides rapid feedback about the expected performance of a parallelization, based on platform parameters and profiling data of the sequential application on the target processor. The use of the tool and simulator are demonstrated on an MPEG-4 video encoder application and two different platform architectures.

In the past decades, data structure analysis was mainly done at a high level of abstraction in the computer science community. For instance, choosing a linked list as a data structure as opposed to an array for a specific situation, was mainly motivated from a performance point of view under the implicit assumption that the computer platform (that had to run the software) consisted out of one monolithical, physical memory. In the context of mobile, embedded devices, energy consumption is as important as performance. In addition to this, the assumption of one monolithical memory is outdated for many (if not all) current-day platforms! Clearly, there is a need to improve the choices that are made during data structure analysis given specific knowledge of the memory hierarchy of the platform under investigation. We show how memory related energy consumption can heavily be reduced by taking into account the access behaviour of the application on the one hand and the available on-chip and off-chip memory space on the other hand. We do this by exploiting the sparseness that is present in onesteady state of the data structure under investigation. Analytical results show that energy reductions of a factor of 8.7 are feasible in comparison to common data structure implementations. We trade these gains off with on-chip memory space consumption of a custom memory architecture.

In embedded network applications, typically a very large part of the area cost is due to memory units. Also the power for such applications is heavily dominated by the storage and transfers. Given its importance, we have developed a systematic memory management methodology in which the storage related issues are optimized as a first step. In this paper, we present our methodology for embedded network applications. It includes both a dynamic memory management stage, where the data types and virtual memory managers are defined, and a physical memory management stage, where the custom memory architecture is defined. As demonstrated on an industrial example, the application of the methodology results in a heavily power and/or area optimized custom memory architecture for a given application.

In this paper, we present the problem of storage bandwidth optimization (SBO) in VLSI system realizations. Our goal is to minimize the required memory bandwidth within the given cycle budget by adding ordering constraints to the flow graph. This allows the subsequent memory allocation and assignment tasks to come up with a cheaper memory architecture with less memories and memory ports. The importance and the effect of SBO is shown on realistic examples both in the video and asynchronous transfer-mode (ATM) domains. We show that it is important to take into account which data is being accessed in parallel, instead of only considering the number of simultaneous memory accesses. Our problem formulation leads to the optimization of a conflict (hyper) graph. For the target domain of ATM, only flat graphs without loops have to be treated. For this subproblem, a prototype tool has been implemented to demonstrate the feasibility of automating this important system design step.

Matisse is a design flow intended for developing embedded systems characterize dby tight inter action b etwe encontrol and data-flow behavior, intensive data storage and tr ansfer, dynamic creation of data, and stringent real-time requirements. Matisse bridges the gap from a system specification, using a cocurr ent obje ct-oriented language, to an optimize d embedded single-chip HW/SW implementation. Matisse supp orts stepwise system-level exploration and refinement, memory architecture exploration, and gradualincorporation of timing constr aints b efore going to tr aditional tools for HW synthesis, SW compilation, and HW/SW interprocessor communication synthesis. Application of Matisse on telecom protocol processing systems shows significant improvements in area usage and power c onsumption.

Efficient use of an optimized custom memory hierarchy to exploit temporal locality in the data accesses can have a very large impact on the power consumption in data dominated applications. In the past, experiments have demonstrated that this task is crucial in a complete low-power memory management methodology. But effective formalized techniques to deal with this specific task have not been addressed yet. In this paper, the surprisingly large design freedom available for the basic problem is explored in-depth and the outline of a systematic solution methodology is proposed. The efficiency of the methodology is illustrated on a real-life motion estimation application. The results obtained for this application show power reductions of about 85% for the memory subsystem compared to the case without a custom memory hierarchy. These large gains justify that data reuse and memory hierarchy decisions should be taken early in the design flow.

Article Formalized methodology for data reuse exploration in hierarchical memory mappings Share on Authors: J. Ph. Diguet IMEC, Kapeldreef 75, B-3001 Leuven, Belgium IMEC, Kapeldreef 75, B-3001 Leuven, BelgiumView Profile , S. Wuytack IMEC, Kapeldreef 75, B-3001 Leuven, Belgium IMEC, Kapeldreef 75, B-3001 Leuven, BelgiumView Profile , F. Catthoor IMEC, Kapeldreef 75, B-3001 Leuven, Belgium IMEC, Kapeldreef 75, B-3001 Leuven, BelgiumView Profile , H. De Man IMEC, Kapeldreef 75, B-3001 Leuven, Belgium IMEC, Kapeldreef 75, B-3001 Leuven, BelgiumView Profile Authors Info & Claims ISLPED '97: Proceedings of the 1997 international symposium on Low power electronics and designAugust 1997 Pages 30–35https://doi.org/10.1145/263272.263278Online:01 August 1997Publication History 46citation185DownloadsMetricsTotal Citations46Total Downloads185Last 12 Months1Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In this paper we present our power exploration methodology for data dominated video applications. This formalized methodology is based on the observation that for this type of application the power consumption is dominated by the memory architecture. Hence, the first exploration stage should be to come up with an optimized memory organisation. Other important observations are that the power consumption of the address generators is of the same magnitude as that of the data-paths and that the address generators are better optimized using specialized techniques.

In this paper, we present a novel approach to model the search space for the custom implementation of set data types, a data type that is commonly found in important application domains such as network component realizations and database applications. The main objective is to arrive at power efficient realizations of these data types in custom data structures, but the model can also be used with nonpower cost functions. Based on the model, we propose an efficient optimization method for finding the implementation with minimum power consumption without performing an exhaustive scan of the search space. The range of power costs for different solutions can easily span four orders of magnitude, so a near optimal solution is crucial. This paper also strongly contributes to our overall goal of a higher level of specification and shorter design cycles for table-based memory organizations for applications where these data types are frequently used. The proposed model and methodology are suited for both hardware and software realizations.

Telecommunication network management applications often require application-specific ICs that use large dynamically allocated stored data structures. Currently available hardware synthesis environments typically do not support dynamic data structure concepts and their associated memory synthesis problems. In this paper we address the background memory management task in a hardware design trajectory, which includes allocation of a distributed memory architecture, assignment and mapping of abstract data structures to memories, and synthesis of dynamic management behavior. With this approach to explore for the optimal memory architecture, the design entry point is lifted to a higher level than currently used for behavioral synthesis, as the specification can be a high-level program using data abstraction. The power of our approach will be substantiated on an industrial high-performance telecommunication ASIC design.