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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]

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]

In this paper, we propose a novel sorting algorithm that sorts input data integer elements on-the-fly without any comparison operations between the data—comparison-free sorting. We present a complete hardware structure, associated timing diagrams, and a formal mathematical proof, which show an overall sorting time, in terms of clock cycles, that is linearly proportional to the number of inputs, giving a speed complexity on the order of O(N). Our hardware-based sorting algorithm precludes the need for SRAM-based memory or complex circuitry, such as pipelining structures, but rather uses simple registers to hold the binary elements and the elements’ associated number of occurrences in the input set, and uses matrix-mapping operations to perform the sorting process. Thus, the total transistor count complexity is on the order of O(N). We evaluate an application-specified integrated circuit design of our sorting algorithm for a sample sorting of N = 1024 elements of size K = 10-bit using 90-nm Taiwan Semiconductor Manufacturing Company (TSMC) technology with a 1 V power supply. Results verify that our sorting requires approximately 4– 6~\mu \texts to sort the 1024 elements with a clock cycle time of 0.5 GHz, consumes 1.6 mW of power, and has a total transistor count of less than 750 000. [ABSTRACT FROM PUBLISHER]

Networks-on-chips (NoCs) are an increasingly popular communication infrastructure in single chip VLSI design for enhancing parallelism and system scalability. Processing elements (PEs) connect to a communication topology via NoC switches, which are responsible for runtime establishment and management of inter-PE communication channels. Since NoC switch design directly affects overall system performance and exploited communication parallelism, much previous work focused on efficient NoC switch design. In this paper, we present MACS—a highly parametric NoC switch architecture that provides reduced data transfer latency, increased designer flexibility, and scalability as compared to previous architectures by combining and enhancing several NoC design strategies. MACS enhances inter-PE communication using a circuit switching technique with minimal adaptive routing and a simple and fair path resolution algorithm to maximize bandwidth utilization. We evaluate area and performance of an FPGA implementation of MACS, and, show that compared to previous work, MACS offers a 2x to 7x decrease in average channel setup latency, a 1.7x to 2x reduction in area requirements, similar average packet latency, up to a 6x increase in the network saturation point, and up to a 1.4x increase in bandwidth utilization. Additionally, we illustrate MACS’s low average channel setup latency using six network traffic patterns and eight parallel JPEG decompression core trace simulations. [ABSTRACT FROM AUTHOR]