Exploiting runtime memory access traces can be a complementary approach to compiler optimizations for the energy reduction in memory hierarchy. This is particularly important for emerging multimedia applications since they usually have input-sensitive runtime behavior which results in dynamic and/or irregular memory access patterns. These types of applications are normally hard to optimize by static compiler optimizations. The reason is that their behavior stays unknown until runtime and may even change during computation. To tackle this problem, we propose an integrated approach of software [compiler and operating system (OS)] and hardware (data access record table) techniques to exploit data reusability of multimedia applications in Multiprocessor Systems on Chip. Guided by compiler analysis for generating scratch pad data layouts and hardware components for tracking dynamic memory accesses, the scratch pad data layout adapts to an input data pattern with the help of a runtime scratch pad memory manager incorporated in the OS. The runtime data placement strategy presented in this paper provides efficient scratch pad utilization for the dynamic applications. The goal is to minimize the amount of accesses to the main memory over the entire runtime of the system, which leads to a reduction in the energy consumption of the system. Our experimental results show that our approach is able to significantly improve the energy consumption of multimedia applications with dynamic memory access behavior over an existing compiler technique and an alternative hardware technique.

Horizontally partitioned caches (HPCs) are a power-efficient architectural feature in which the processor maintains two or more data caches at the same level of hierarchy. HPCs help reduce cache pollution and thereby improve performance. Consequently, most previous research has focused on exploiting HPCs to improve performance and achieve energy reduction only as a byproduct of performance improvement. However, with energy consumption becoming the first class design constraint, there is an increasing need for compilation techniques aimed at energy reduction itself. This paper proposes and explores several low-complexity algorithms aimed at reducing the energy consumption. Acknowledging that the compiler has a significant impact on the energy consumption of the HPCs, Compiler-in-the-Loop Design Space Exploration methodologies are also presented to carefully choose the HPC parameters that result in minimum energy consumption for the application.

With advances in process technology, soft errors are becoming an increasingly critical design concern. Owing to their large area, high density, and low operating voltages, caches are worst hit by soft errors. Based on the observation that in multimedia applications, not all data require the same amount of protection from soft errors, we propose a partially protected cache (PPC) architecture, in which there are two caches, one protected and the other unprotected at the same level of memory hierarchy. We demonstrate that as compared to the existing unprotected cache architectures, PPC architectures can provide 47 times reduction in failure rate, at only 1% runtime and 3% power overheads. In addition, the failure rate reduction obtained by PPCs is very sensitive to the PPC cache configuration. Therefore, this observation provides an opportunity for further improvement of the solution by correctly parameterizing the PPC configurations. Consequently, we develop design space exploration (DSE) strategies to discover the best PPC configuration. Our DSE technique can reduce the exploration time by more than six times as compared to an exhaustive approach.

Horizontally Partitioned Caches (HPCs) are a promising architectural feature to reduce the energy consumption of the memory subsystem. However, the energy reduction obtained using HPC architectures is very sensitive to the HPC parameters. Therefore it is very important to explore the HPC design space and carefully choose the HPC parameters that result in minimum energy consumption for the application. However, since in HPC architectures, the compiler has a significant impact on the energy consumption of the memory subsystem, it is extremely important to include compiler while deciding the HPC design parameters. While there has been no previous approaches to HPC design exploration, existing cache design space exploration methodologies do not include the compiler effects during DSE. In this paper, we present a Compiler-in- the-Loop (CIL) Design Space Exploration (DSE) methodology to explore and decide the HPC design parameters. Our experimental results on HP iPAQ h4300-like memory subsystem running benchmarks from the MiBench suite demonstrate that CIL DSE can discover HPC configurations with up to 80% lesser energy consumption than the HPC configuration in the iPAQ. In contrast, tradition simulation-only exploration can discover HPC design parameters that result in only 57% memory subsystem energy reduction. Finally our hybrid CIL DSE heuristic saves 67% of the exploration time as compared to the exhaustive exploration, while providing maximum possible energy savings on our set of benchmarks.

Embedded multimedia applications consist of regular and irregular memory access patterns. Particularly, irregular pattern are not amenable to static analysis for extraction of access patterns, and thus prevent efficient use of a Scratch Pad Memory (SPM) hierarchy for performance and energy improvements. To resolve this, we present a compiler strategy to optimize data layout in regular/irregular multimedia applications running on embedded multiprocessor environments. The goal is to maximize the amount of accesses to the SPM over the entire system which leads to a reduction in the energy consumption of the system. This is achieved by optimizing data placement of application-wide reused data so that it resides in the SPMs of processing elements. Specifically, our scheme is based on a profiling that generates a memory access footprint. The memory access footprint is used to identify data elements with fine granularity that can profitably be placed in the SPMs to maximize performance and energy gains. We present a heuristic approach that efficiently exploits the SPMs using memory access footprint. Our experimental results show that our approach is able to reduce energy consumption by 30% and improve performance by 18% over cache based memory subsystems for various multimedia applications.

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.

Many embedded array-intensive applications have irregular access patterns that are not amenable to static analysis for extraction of access patterns, and thus prevent efficient use of a Scratch Pad Memory (SPM) hierarchy for performance and power improvement. We present a profiling based strategy that generates a memory access trace which can be used to identify data elements with fine granularity that can profitably be placed in the SPMs to maximize performance and energy gains. We developed an entire toolchain that allows incorporation of the code required to profitably move data to SPMs; visualization of the extracted access pattern after profiling; and evaluation/exploration of the generated application code to steer mapping of data to the SPM to yield performance and energy benefits.We present a heuristic approach that efficiently exploits the SPM using the profiler-driven access pattern behaviors. Experimental results on EEMBC and other industrial codes obtained with our framework show that we are able to achieve 36% energy reduction and reduce execution time by up to 22% compared to a cache based system.

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.

With advances in process technology, soft errors(SE)are becoming an increasingly critical design concern. Due to their large area and high density, caches are worst hit by soft errors. Although Error Correction Code based mechanisms protect the data in caches, they have high performance and power overheads. Since multimedia applications are increasingly being used in mission-critical embedded systems where both reliability and energy are a major concern, there is a de?nite need to improve reliability in embedded systems, without too much energy overhead. We observe that while a soft error in multimedia data may only result in a minor loss in QoS, a soft error in avariable that controls the execution ?ow of the program may be fatal. Consequently, we propose to partition the data space into failure critical and failure non-critical data, and provide a high-degree of soft error protection only to the failure critical data in Horizontally Partitioned Caches. Experimental results demonstrate that our selective data protection can achieve the failure rate close to that of a soft error protected cache system, while retaining the performance and energy consumption similar to those of a traditional cache system, with some degradation in QoS. For example, for conventional con?guration as in IntelXScale, our approach achieves the same failure rate, while improving performance by 28% and reducing energy consumption by 29%in comparison with a soft error protected cache.

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.

Horizontally partitioned data caches are a popular architectural feature in which the processor maintains two or more data caches at the same level of hierarchy. Horizontally partitioned caches help reduce cache pollution and thereby improve performance. Consequently most previous research has focused on exploiting horizontally partitioned data caches to improve performance, and achieve energy reduction only as a byproduct of performance improvement. In constrast, in this paper we show that optimizing for performance trades-off several opportunities for energy reduction. Our experiments on a HP iPAQ h4300-like memory subsystem demonstrate that optimizing for energy consumption results in up to 127% less memory subsystem energy consumption than the performance optimal solution. Furthermore, we show that energy optimal solution incurs on average only 1.7% performance penalty. Therefore, with energy consumption becoming a first class design constraint, there is a need for compilation techniques aimed at energy reduction. To achieve aforementioned energy savings we propose and explore several low-complexity algorithms aimed at reducing the energy consumption and show that very simple greedy heuristics achieve 97% of the possible memory subsystem energy savings.

In today's embedded applications, a significant portion of energy is spent in the memory subsystem. Several approaches have been proposed to minimize this energy, including the use of scratch pad memories, with many based on static analysis of a program. However, it is often not possible to perform static analysis and optimization of a program's memory access behavior unless the program is specifically written for this purpose. We introduce the FORAY model of a program that permits aggressive analysis of the application's memory behavior that further enables such optimization since it consists of 'for' loops and array accesses which are easily analyzable. We present FORAY-GEN, an automated profile-based approach for extraction of the FORAY model from the original program. We also demonstrate how FORAY-GEN enhances applicability of other memory subsystem optimization approaches, resulting in an average doubling in the number of memory references that can be analyzed by existing static approaches.

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.

Dynamic voltage scaling (DVS) is a known effective mechanism for reducing CPU energy consumption without significant performance degradation. While a lot of work has been done on inter-task scheduling algorithms to implement DVS under operating system control, new research challenges exist in intra-task DVS techniques under software and compiler control. In this paper we introduce a novel intra-task DVS technique under compiler control using program checkpoints. Checkpoints are generated at compile time and indicate places in the code where the processor speed and voltage should be re-calculated. Checkpoints also carry user-defined time constraints. Our technique handles multiple intra-task performance deadlines and modulates power consumption according to a run-time power budget. We experimented with two heuristics for adjusting the clock frequency and voltage. For the particular benchmark studied, one heuristic yielded 63% more energy savings than the other. With the best of the heuristics we designed, our technique resulted in 82% energy savings over the execution of the program without employing DVS.