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.

Using software-controlled Scratch-Pad Memory (SPM) in Systems-on-Chip has the potential of reducing power consumption by using design-time application knowledge to reduce memory accesses and processor stalls. This paper presents a fully automatic application analysis and transformation tool which selects data-structures for transfer to the SPM and schedules data transfers between background memory and SPM (pre-fetching) to achieve both high performance and low power consumption. A case study applying this tool on an MPEG-4 video encoder shows an overall power reduction of 25%, a 40% power reduction in just the memories and a 40% reduction in processor cycles as compared to an optimized hardware-cache based solution.

As technology advances, it becomes feasible to implement a large multiprocessor systems-on-chip (MPSoCs) to satisfy the increased performance demands of embedded applications. The increased complexity of systems leads to an increased power consumption. Reducing the consumption is an important task, considering that the available power may be limited in battery-operated embedded systems. The selection of memory and communication architectures affects the power efficiency of the design. In this paper, we propose a novel approach that enables the energy-aware cosynthesis of both memory and communication architectures for streaming applications. As opposed to earlier techniques, we propose a powerful compile-time analysis of memory access behavior in multiprocessor systems, which adds flexibility in selecting scratch-pad-based memory architectures. We propose and compare three memory/communication synthesis techniques, namely, an optimal mixed integer-linear-programming (ILP)-based cosynthesis technique, a mixed ILP (MILP)-based traditional two-step synthesis approach, where memory and communication synthesis is sequentially performed, and a cosynthesis heuristic that synthesizes energy-efficient hierarchical bus-based communication architectures with guaranteed throughput. Our experimental results on a number of streaming applications show that both the traditional two-step synthesis approach and heuristic result in up to 50% worse power consumption in comparison with our proposed cosynthesis approach. However, on some of the streaming benchmarks, our cosynthesis heuristic approach was able to find optimal or near-optimal results in a much shorter time than the MILP cosynthesis approach.

Multi-processor systems-on-chip use networks-on-chip (NoC) as a communication backbone to tackle the communication between processors and multi-level memory hierarchies. Inter-processor communication has a high impact on the NoC traffic but, to this day, there have been few detailed studies. Based on a realistic case study, we present a contrastive comparison of cache-based versus scratch-pad managed inter-processor communication for (distributed shared-memory) multiprocessor systems-on-chip. The platforms we target use six DSP nodes and a shared L2 memory, interconnected by a packet-switched network-on-chip with differentiated services. The first version of the platform uses caches to perform inter-processor communication whereas the second one uses a novel type of distributed DMA to help performing scratch-pad management. With detailed simulation results we show that the scratchpad application mapping has the best overall performance, that it helps smoothing NoC traffic and that it is not sensitive to the quality-of-service (QoS) used. We furthermore demonstrate that, on the contrary, cache-based MP-SoCs are very sensitive to the QoS level and that they generate significantly more NoC traffic than their scratch-pad counterpart. We recommend, where possible, to use scratch-pad management for NoC supported MP-SoCs as it yields performant, predictable results and can benefit from platform virtualization to achieve composability of applications

In multimedia and other streaming applications, a significant portion of energy is spent on data transfers. Exploiting data reuse opportunities in the application, we can reduce this energy by making copies of frequently used data in a small local memory and replacing speed- and power-inefficient transfers from main off-chip memory by more efficient local data transfers. In this article we present an automated approach for analyzing these opportunities in a program that allows modification of the program to use custom scratch-pad memory configurations comprising a hierarchical set of buffers for local storage of frequently reused data. Using our approach we are able to both reduce energy consumption of the memory subsystem when using a scratch-pad memory by about a factor of two, on average, and improve memory system performance compared to a cache of the same size.

Loop fusion and loop shifting are important transformations for improving data locality to reduce the number of costly accesses to off-chip memories. Since exploring the exact platform mapping for all the loop transformation alternatives is a time consuming process, heuristics steered by improved data locality are generally used. However, pure locality estimates do not sufficiently take into account the hierarchy of the memory platform. This paper presents a fast, incremental technique for hierarchical memory size requirement estimation for loop fusion and loop shifting at the early loop transformations design stage. As the exact memory platform is often not yet defined at this stage, we propose a platform-independent approach which reports the Pareto-optimal trade-off points for scratch-pad memory size and off-chip memory accesses. The estimation comes very close to the actual platform mapping. Experiments on realistic test-vehicles confirm that. It helps the designer or a tool to find the interesting loop transformations that should then be investigated in more depth afterward.

The increasing use of multiprocessor systems-on-chip (MPSoCs) for high performance demands of embedded applications results in high power dissipation. The memory subsystem is a large and critical contributor to both energy and performance, requiring system designers to perform exploration of low power memory organizations. In this paper we present a novel multiprocessor data reuse analysis technique that allows the system designer to explore a wide range of customized memory hierarchy organizations with different size and energy profiles. Our technique enables the system designer to explore feasible memory subsystem solutions that meet power and area constraints while maintaining the necessary performance level. Our experiments on the complex QSDPCM benchmark illustrate the exploration of a wide range of customized memory hierarchies for an MPSoC implementation.

Memory latency has always been a major issue in embedded systems that execute memory-intensive applications. This is even more true as the gap between processor and memory speed continues to grow. Hardware and software prefetching have been shown to be effective in tolerating the large memory latencies inherit in large off-chip memories; however, both types of prefetching have their shortcomings. Hardware schemes are more complex and require extra circuitry to compute data access strides, while software schemes generate prefetch instructions, which if not computed carefully may hamper performance. On the other hand, some applications domains (such as multimedia) have a uniform and known a priori memory access pattern, that if exploited, could yield significant application performance improvement. With this characteristic in mind, we present our findings on hiding memory latency using the direct memory access (DMA) mode, which is present in all modern systems, combined with a software prefetch mechanism, and a customized on-chip memory hierarchy mapping. Compared to previous approaches, we are able to estimate the performance and power metrics, without actually implementing the embedded system. Experimental results on nine well known multimedia and imaging applications prove the efficiency of our technique. Finally, we verify the performance estimations by implementing and simulating the algorithms on the TI C6201 processor.

The memory subsystem has always been a bottleneck in performance as well as significant power contributor in memory intensive applications. Many researchers have presented multi-layered memory hierarchies as a means to design energy and performance efficient systems. However, most of the previous work does not explore trade-offs systematically. We fill this gap by proposing a formalized technique that takes into consideration data reuse, limited life-time of the arrays of an application and application specific prefetching opportunities, and performs a thorough tradeoff exploration for different memory layer sizes. This technique has been implemented on a prototype tool, which was tested successfully using nine real-life applications of industrial relevance. Following this approach we have able to reduce execution time up to 60%, and energy consumption up to 70%.

In multimedia and other streaming applications a significant portion of energy is spent on data transfers. Exploiting data reuse opportunities in the application, we can reduce this energy by making copies of frequently used data in a small local memory and replacing speed and power inefficient transfers from main off-chip memory by more efficient local data transfers. In this paper we present an automated approach for analyzing these opportunities in a program that allows modification of the program to use custom scratch pad memory configurations comprising a hierarchical set of buffers for local storage of frequently reused data. Using our approach we are able to reduce energy consumption of the memory subsystem when using a scratch pad memory by a factor of two on average compare to a cache of the same size.

An important problem in extracting maximum benefits from an SDRAM-based architecture is to exploit data locality at the page granularity. Frequent switches between data pages can increase memory latency and have an impact on energy consumption. In this paper, we propose a mathematical formulation, using Presburger arithmetic and Ehrhart polynomials to estimate the number of page breaks statically (i.e., at compile time). The results obtained using video codes indicate that the proposed framework can estimate the number of page breaks with good accuracy.

We present a survey of the state-of-the-art techniques used in performing data and memory-related optimizations in embedded systems. The optimizations are targeted directly or indirectly at the memory subsystem, and impact one or more out of three important cost metrics: area, performance, and power dissipation of the resulting implementation. We first examine architecture-independent optimizations in the form of code transoformations. We next cover a broad spectrum of optimization techniques that address memory architectures at varying levels of granularity, ranging from register files to on-chip memory, data caches, and dynamic memory (DRAM). We end with memory addressing related issues.

In contrast to current design practice for (programmable) processor mapping, which mainly targets performance, we focus on a systematic trade-off between cycle budget and energy consumed in the background memory organization. The latter is a crucial component in many of todays designs, including multi-media, network protocols and telecom signal processing. We have a systematic way and tool to explore both freedoms and to arrive at Pareto charts, in which for a given application the lowest cost implementation of the memory organization is plotted against the available cycle budget per submodule. This by making optimal usage of a parallelized memory architecture. We indicate, with results on a digital audio broadcasting receiver and an image compression demonstrator, how to effectively use the Pareto plot to gain significantly in overall system energy consumption within the global real-time constraints.

Successful exploration of system-level design decisions is impossible without fast and accurate estimation of the impact on the system cost.In most multimedia applications, the dominant cost factor is related to the organization of the memory architecture.This paper presents a systematic approach which allows effective system-level exploration of memory organization design alternatives, based on accurate feedback by using our earlier developed tools.The effectiveness of this approach is illustrated on an industrial application.Applying our approach, a substantial part of the design search space has been explored in a very short time, resulting in a cost-efficient solution which meets all design constraints.

Data transfers and storage are crucial cost factors in multimedia systems. Systematic methodologies are needed to obtain dramatic reductions in terms of power, area and cycle count. Upcoming multimedia processing applications will require high memory bandwidth. In this paper, we estimate that a software reference implementation of an MPEG-4 video encoder typically requires five Gtransfers/s to main memory for a simple profile level L2. This shows a clear need for optimization and the use of intermediate memory stages. By applying our ACROPOLIS methodology, developed mainly to relieve this data access bottleneck, we have arrived at an implementation which needs a factor 65 less background accesses. In addition, we also show that we can heavily improve on the memory transfers, without sacrificing speed (even gaining about 10% on cache misses and cycles for a DEC Alpha), by aggressive source code transformations.