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The Internet of Things (IoT) refers to a pervasive presence of interconnected and uniquely identifiable physical devices. These devices’ goal is to gather data and drive actions in order to improve productivity, and ultimately reduce or eliminate reliance on human intervention for data acquisition, interpretation, and use. The proliferation of these connected low-power devices will result in a data explosion that will significantly increase data transmission costs with respect to energy consumption and latency. Edge computing reduces these costs by performing computations at the edge nodes, prior to data transmission, to interpret and/or utilize the data. While much research has focused on the IoT’s connected nature and communication challenges, the challenges of IoT embedded computing with respect to device microprocessors has received much less attention. This paper explores IoT applications’ execution characteristics from a microarchitectural perspective and the microarchitectural characteristics that will enable efficient and effective edge computing. To tractably represent a wide variety of next-generation IoT applications, we present a broad IoT application classification methodology based on application functions, to enable quicker workload characterizations for IoT microprocessors. We then survey and discuss potential microarchitectural optimizations and computing paradigms that will enable the design of right-provisioned microprocessors that are efficient, configurable, extensible, and scalable. This paper provides a foundation for the analysis and design of a diverse set of microprocessor architectures for next-generation IoT devices. [ABSTRACT FROM PUBLISHER]

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]

We present a new comparator design featuring wide-range and high-speed operation using only conventional digital CMOS cells. Our comparator exploits a novel scalable parallel prefix structure that leverages the comparison outcome of the most significant bit, proceeding bitwise toward the least significant bit only when the compared bits are equal. This method reduces dynamic power dissipation by eliminating unnecessary transitions in a parallel prefix structure that generates the N-bit comparison result after \lceil\log4N\rceil+\lceil\log16N\rceil+4 CMOS gate delays. Our comparator is composed of locally interconnected CMOS gates with a maximum fan-in and fan-out of five and four, respectively, independent of the comparator bitwidth. The main advantages of our design are high speed and power efficiency, maintained over a wide range. Additionally, our design uses a regular reconfigurable VLSI topology, which allows analytical derivation of the input-output delay as a function of bitwidth. HSPICE simulation for a 64-b comparator shows a worst case input-output delay of 0.86 ns and a maximum power dissipation of 7.7 mW using 0.15-\mum TSMC technology at 1 GHz. [ABSTRACT FROM AUTHOR]

Since multicore architectures are becoming more popular, recent multicore optimizations focus on energy consumption. In this paper, we focus on reducing the energy consumption in the data and instruction cache hierarchies in a multicore system. First, we present a level one data cache tuning heuristic for a heterogeneous multicore system, which classifies applications based on data sharing and cache behavior and uses this classification to guide cache tuning and reduce the number of cores that need to be tuned. Results reveal average energy savings of 25 percent for 2, 4, 8, and 16-core systems while searching only 1 percent of the design space. Next, we present a level one instruction cache tuning heuristic that reduces energy consumption in the instruction cache hierarchy by an average of 53 percent for 2, 4, 8, and 16-core systems, while searching less than 1 percent of the design space. Finally, we develop a custom, global hardware cache tuner for a dual-core system and show that our cache tuner has low area, energy, and power overheads. [ABSTRACT FROM PUBLISHER]

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]

The instruction cache is a popular optimization target due to the cache's high impact on system performance and power and because of the cache's predictable temporal and spatial locality. This article is an in depth study on the interaction of code reordering (a long-known technique) and cache configuration (a relatively new technique). Experimental results show that code reordering coupled with cache configuration reveals additional energy savings as high as 10-15% for several benchmarks with reduced cache area as high as 48%. To exploit these additional benefits, we architect and evaluate several design exploration heuristics for combining these two methods. [ABSTRACT FROM AUTHOR]

In recent years, efficient dynamic reconfiguration techniques have been widely employed for system optimization. Dynamic cache reconfiguration is a promising approach for reducing energy consumption as well as for improving overall system performance. It is a major challenge to introduce cache reconfiguration into real-time multitasking systems, since dynamic analysis may adversely affect tasks with timing constraints. This article presents a novel approach for implementing cache reconfiguration in soft real-time systems by efficiently leveraging static analysis during runtime to minimize energy while maintaining the same service level. To the best of our knowledge, this is the first attempt to integrate dynamic cache reconfiguration in real-time scheduling techniques. Our experimental results using a wide variety of applications have demonstrated that our approach can significantly reduce the cache energy consumption in soft real-time systems (up to 74%). [ABSTRACT FROM AUTHOR]

Tuning a configurable cache subsystem to an application can greatly reduce memory hierarchy energy consumption. Previous tuning methods use a level one configurable cache only, or a second level with separate instruction and data configurable caches. We instead use a commercially-common unified second level cache, a seemingly minor difference that actually expands the configuration space from 500 to about 20000. We develop additive way tuning for tuning a cache subsystem with this large space, yielding 61% energy savings and 9% performance improvements over a nonconfigurable cache, greatly outperforming an extension of a previous method. [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]