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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]

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]

Technological advancements in the silicon industry, as predicted by Moore's law, have enabled integration of billions of transistors on a single chip. To exploit this high transistor density for high performance, embedded systems are undergoing a transition from single-core to multi-core. Although a majority of embedded wireless sensor networks (EWSNs) consist of single-core embedded sensor nodes, multi-core embedded sensor nodes are envisioned to burgeon in selected application domains that require complex in-network processing of the sensed data. In this paper, we propose an architecture for heterogeneous hierarchical multi-core embedded wireless sensor networks (MCEWSNs) as well as an architecture for multi-core embedded sensor nodes used in MCEWSNs. We elaborate several compute-intensive tasks performed by sensor networks and application domains that would especially benefit from multi-core embedded sensor nodes. This paper also investigates the feasibility of two multi-core architectural paradigms—symmetric multiprocessors (SMPs) and tiled many-core architectures (TMAs)—for MCEWSNs. We compare and analyze the performance of an SMP (an Intel-based SMP) and a TMA (Tilera's TILEPro64) based on a parallelized information fusion application for various performance metrics (e.g., runtime, speedup, efficiency, cost, and performance per watt). Results reveal that TMAs exploit data locality effectively and are more suitable for MCEWSN applications that require integer manipulation of sensor data, such as information fusion, and have little or no communication between the parallelized tasks. To demonstrate the practical relevance of MCEWSNs, this paper also discusses several state-of-the-art multi-core embedded sensor node prototypes developed in academia and industry. We further discuss research challenges and future research directions for MCEWSNs. [ABSTRACT FROM AUTHOR]

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]

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]

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]