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Caches are commonly used to bridge the processor-memory performance gap in embedded systems. Since embedded systems typically have stringent design constraints imposed by physical size, battery capacity, and real-time deadlines much research focuses on cache optimizations, such as improved performance and/or reduced energy consumption. Cache locking is a popular cache optimization that loads and retains/locks selected memory contents from an executing application into the cache to increase the cache’s predictability. Previous work has shown that cache locking also has the potential to improve cache energy consumption. In this paper, we introduce phase-based cache locking, PhLock, which leverages an application’s varying runtime characteristics to dynamically select the locked memory contents to optimize cache energy consumption. Using a variety of applications from the SPEC2006 and MiBench benchmark suites, experimental results show that PhLock is promising for reducing both the instruction and data caches’ energy consumption. As compared to a nonlocking cache, PhLock reduced the instruction and data cache energy consumption by an average of 5% and 39%, respectively, for SPEC2006 applications, and by 75% and 14%, respectively, for MiBench benchmarks. [ABSTRACT FROM PUBLISHER]

As the number of cores in chip multi-processor systems increases, the contention over shared last-level cache (LLC) resources increases, thus making LLC optimization critical, especially for embedded systems with strict area/energy/power constraints. We propose cache partitioning with partial sharing (CaPPS), which reduces LLC contention using cache partitioning and improves utilization with sharing configuration. Sharing configuration enables the partitions to be privately allocated to a single core, partially shared with a subset of cores, or fully shared with all cores based on the co-executing applications' requirements. CaPPS imposes low hardware overhead and affords an extensive design space to increase optimization potential. To facilitate fast design space exploration, we develop an analytical model to quickly estimate the miss rates of all CaPPS configurations using the applications' isolated LLC access traces to predict runtime LLC contention. Experimental results demonstrate that the analytical model estimates cache miss rates with an average error of only 0.73 % and with an average speedup of $$3505\times $$ as compared to a cycle-accurate simulator. Due to CaPPS's extensive design space, CaPPS can reduce the average LLC miss rate by as much as 25 % as compared to baseline configurations and as much as 14-17 % as compared to prior works. [ABSTRACT FROM AUTHOR]

Networked embedded systems typically leverage a collection of low-power embedded systems (nodes) to collaboratively execute applications spanning diverse application domains (e.g., video, image processing, communication, etc.) with diverse application requirements. The individual networked nodes must operate under stringent constraints (e.g., energy, memory, etc.) and should be specialized to meet varying applications' requirements in order to adhere to these constraints. Phase-based tuning specializes a system's tunable parameters to the varying runtime requirements of an application's different phases of execution to meet optimization goals. Since the design space for tunable systems can be very large, one of the major challenges in phase-based tuning is determining the best configuration for each phase without incurring significant tuning overhead (e.g., energy and/or performance) during design space exploration. In this paper, we propose phase distance mapping, which directly determines the best configuration for a phase, thereby eliminating design space exploration. Phase distance mapping applies the correlation between a known phase's characteristics and best configuration to determine a new phase's best configuration based on the new phase's characteristics. Experimental results verify that our phase distance mapping approach, when applied to cache tuning, determines cache configurations within 1 % of the optimal configurations on average and yields an energy delay product savings of 27 % on average. [ABSTRACT FROM AUTHOR]

Abstract: With Moore’s law supplying billions of transistors on-chip, embedded systems are undergoing a transition from single-core to multi-core to exploit this high transistor density for high performance. However, the optimal layout of these multiple cores along with the memory subsystem (caches and main memory) to satisfy power, area, and stringent real-time constraints is a challenging design endeavor. The short time-to-market constraint of embedded systems exacerbates this design challenge and necessitates the architectural modeling of embedded systems to reduce the time-to-market by expediting target applications to device/architecture mapping. In this paper, we present a queueing theoretic approach for modeling multi-core embedded systems that provides a quick and inexpensive performance evaluation both in terms of time and resources as compared to the development of multi-core simulators and running benchmarks on these simulators. We verify our queueing theoretic modeling approach by running SPLASH-2 benchmarks on the SuperESCalar simulator (SESC). Results reveal that our queueing theoretic model qualitatively evaluates multi-core architectures accurately with an average difference of 5.6% as compared to the architectures’ evaluations from the SESC simulator. Our modeling approach can be used for performance per watt and performance per unit area characterizations of multi-core embedded architectures, with varying number of processor cores and cache configurations, to provide a comparative analysis. [Copyright &y& Elsevier]

Technological advancements in the silicon industry, as predicted by Moore’s law, have resulted in an increasing number of processor cores on a single chip, giving rise to multicore, and subsequently many-core architectures. This work focuses on identifying key architecture and software optimizations to attain high performance from tiled many-core architectures (TMAs)—an architectural innovation in the multicore technology. Although embedded systems design is traditionally power-centric, there has been a recent shift toward high-performance embedded computing due to the proliferation of compute-intensive embedded applications. The TMAs are suitable for these embedded applications due to low-power design features in many of these TMAs. We discuss the performance optimizations on a single tile (processor core) as well as parallel performance optimizations, such as application decomposition, cache locality, tile locality, memory balancing, and horizontal communication for TMAs. We elaborate compiler-based optimizations that are applicable to TMAs, such as function inlining, loop unrolling, and feedback-based optimizations. We present a case study with optimized dense matrix multiplication algorithms for Tilera’s TILEPro64 to experimentally demonstrate the performance and performance per watt optimizations on TMAs. Our results quantify the effectiveness of algorithmic choices, cache blocking, compiler optimizations, and horizontal communication in attaining high performance and performance per watt on TMAs. [ABSTRACT FROM AUTHOR]

Abstract: Technological advancements in embedded systems due to Moore's law have led to the proliferation of wireless sensor networks (WSNs) in different application domains (e.g., defense, health care, surveillance systems) with different application requirements (e.g., lifetime, reliability). Many commercial-off-the-shelf (COTS) sensor nodes can be specialized to meet these requirements using tunable parameters (e.g., processor voltage and frequency) to specialize the operating state. Since a sensor node's performance depends greatly on environmental stimuli, dynamic optimizations enable sensor nodes to automatically determine their operating state in situ. However, dynamic optimization methodology development given a large design space and resource constraints (memory and computational) is an extremely challenging task. In this paper, we propose a lightweight dynamic optimization methodology that intelligently selects initial tunable parameter values to produce a high-quality initial operating state in one-shot for time-critical or highly constrained applications. Further operating state improvements are made using an efficient greedy exploration algorithm, achieving optimal or near-optimal operating states while exploring only 0.04% of the design space on average. We also propose an application metrics estimation model, which is leveraged by our dynamic optimization methodology, to estimate high-level application metrics (e.g., lifetime, throughput) from sensor node tunable parameters and hardware specific internals. [Copyright &y& Elsevier]

Loop caches provide an effective method for decreasing memory hierarchy energy consumption by storing frequently executed code (critical regions) in a more energy efficient structure than the level one cache. However, due to code structure restrictions or costly design time pre-analysis efforts, previous loop cache designs are not suitable for all applications and system scenarios. We present an adaptive loop cache that is amenable to a wider range of system scenarios, which can provide an additional 20% average instruction cache energy savings (with individual benchmark energy savings as high as 69%) compared to the next best loop cache, the preloaded loop cache. [ABSTRACT FROM AUTHOR]

Dynamic software optimization methods are becoming increasingly popular for improving software performance and power. The first step in dynamic optimization consists of detecting frequently executed code, or "critical regions." Most previous critical region detectors have been targeted to desktop processors. We introduce a critical region detector targeted to embedded processors, with the unique features of being very size and power efficient and being completely nonintrusive to the software's execution-features needed in timing-sensitive embedded systems. Our detector not only finds the critical regions, but also determines their relative frequencies, a potentially important feature for selecting among alternative dynamic optimization methods. Our detector uses a tiny cache-like structure coupled with a small amount of logic. We provide results of extensive explorations across 19 embedded system benchmarks. We show that highly accurate results can be achieved with only a 0.02 percent power overhead, acceptable size overhead, and zero runtime overhead. Our detector is currently being used as part of a dynamic hardware/software partitioning approach, but is applicable to a wide variety of situations. [ABSTRACT FROM AUTHOR]

Embedded systems have stringent design constraints, which has necessitated much prior research focus on optimizing energy consumption and/or performance. Since embedded systems typically have fewer cooling options, rising temperature, and thus temperature optimization, is an emergent concern. Most embedded systems only dissipate heat by passive convection, due to the absence of dedicated thermal management hardware mechanisms. The embedded system’s temperature not only affects the system’s reliability, but can also affect the performance, power, and cost. Thus, embedded systems require efficient thermal management techniques. However, thermal management can conflict with other optimization objectives, such as execution time and energy consumption. In this paper, we focus on managing the temperature using a synergy of cache optimization and dynamic frequency scaling, while also optimizing the execution time and energy consumption. This paper provides new insights on the impact of cache parameters on efficient temperature-aware cache tuning heuristics. In addition, we present temperature-aware phase-based tuning, TaPT, which determines Pareto optimal clock frequency and cache configurations for fine-grained execution time, energy, and temperature tradeoffs. TaPT enables autonomous system optimization and also allows designers to specify temperature constraints and optimization priorities. Experiments show that TaPT can effectively reduce execution time, energy, and temperature, while imposing minimal hardware overhead. [ABSTRACT FROM AUTHOR]

With Moore's law supplying billions of transistors on-chip, embedded systems are undergoing a transition from single-core to multicore to exploit this high-transistor density for high performance. Embedded systems differ from traditional high-performance supercomputers in that power is a first-order constraint for embedded systems; whereas, performance is the major benchmark for supercomputers. The increase in on-chip transistor density exacerbates power/thermal issues in embedded systems, which necessitates novel hardware/software power/thermal management techniques to meet the ever-increasing high-performance embedded computing demands in an energy-efficient manner. This paper outlines typical requirements of embedded applications and discusses state-of-the-art hardware/software high-performance energy-efficient embedded computing (HPEEC) techniques that help meeting these requirements. We also discuss modern multicore processors that leverage these HPEEC techniques to deliver high performance per watt. Finally, we present design challenges and future research directions for HPEEC system development. [ABSTRACT FROM AUTHOR]