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computation sharing programmable FIR filter for low-power and high-performance applications

IEEE JOURNAL OF SOLID-STATE CIRCUITS 2004年第2期39卷 348-357页

作者： Park, J Jeong, W Mahmoodi-Meimand, H Wang, YT Choo, H Roy, K Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47906 USA

This paper presents a programmable digital finite-impulse response (FIR) filter for high-performance and low-power applications. The architecture is based on a computation sharing multiplier (CSHM) which specifically targets computation re-use in vector-scalar products and can be effectively used in the low-complexity programmable FIR filter design. Efficient circuit-level techniques, namely a new carry-select adder and conditional capture flip-flop (CCFF), are also used to further in prove power and performance. A 10-tap programmable FIR filter was implemented. and fabricated in CMOS 0.25-mum technology based on the proposed architectural and circuit-level techniques. The chip's core contains approximately 130 K transistors and occupies 9.93 mm(2) area.

关键词： computation sharing dual transition skewed logic programmable finite impulse response (FIR) filter

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Multi-Objective computation sharing in Energy and Delay Constrained Mobile Edge Computing Environments

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2021年第10期20卷 2992-3005页

作者： Bozorgchenani, Arash Mashhadi, Farshad Tarchi, Daniele Monroy, Sergio A. Salinas Univ Bologna Dept Elect Elect & Informat Engn I-40126 Bologna Italy Wichita State Univ Dept Elect Engn & Comp Sci Wichita KS 67260 USA

In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

关键词： Task analysis Energy consumption Delays Mobile handsets Cloud computing Servers Edge computing Mobile edge computing fog computing computation sharing NSGA2 multi-objective optimization evolutionary algorithms energy consumption delay

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CroApp: A CNN-Based Resource Optimization Approach in Edge Computing Environment

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第9期18卷 6300-6307页

作者： Jia, Yongzhe Liu, Bowen Dou, Wanchun Xu, Xiaolong Zhou, Xiaokang Qi, Lianyong Yan, Zheng Nanjing Univ Dept Comp Sci & Technol State Key Lab Novel Software Technol Nanjing 210023 Peoples R China Nanjing Univ Informat Sci & Technol Sch Comp & Software Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China Shiga Univ Fac Data Sci Hikone 5228522 Japan RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan Qufu Normal Univ Sch Comp Sci Rizhao 276826 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Xidian Univ Sch Cyber Engn Xian 710071 Peoples R China Aalto Univ Dept Commun & Networking Espoo 02150 Finland

With the emergence of various convolutional neural network (CNN)-based applications and the rapid growth of CNN model scale, the resource-constricted end devices can hardly deploy CNN-based applications. Current work optimizes the CNN model on edge servers and deploys the optimized model on devices in an edge computing environment. However, most of them only optimize the resource consumption within or across models solely, whereas neglecting the other side. In this article, we propose a novel CNN-based resource optimization approach (CroApp) that not only optimizes the resource consumption within the CNN model but also pays attention to resource optimization across the applications. Specifically, we adopt model compression as the "inner-model" optimization method, as well as computation sharing as the "intermodel" optimization method. First, during "inner-model" optimization, the CroApp prunes unnecessary parameters within the model on edge servers to reduce the scale of the model. Then, during "intermodel" optimization, the CroApp trains a set of shareable models based on the pruned model and sends these shareable models to end devices. Finally, the CroApp adaptively adjusts the shared models to reduce resource consumption. The experimental results show that the CroApp outperforms the state-of-the-art approaches in terms of resource reduction, scalability, and application performance.

关键词： computational modeling Optimization Servers Adaptation models Solid modeling Surveillance Edge computing computation sharing convolutional neural networks (CNNs) edge computing model pruning resource optimization

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A search problem in complex diagnostic Bayesian networks

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KNOWLEDGE-BASED SYSTEMS 2012年 30卷 95-103页

作者： Liu, Dayou Huang, Yuxiao Yu, Qiangyuan Chen, Juan Jia, Haiyang Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Changchun 130012 Peoples R China Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

Inference in Bayesian networks (BNs) is NP-hard. We proposed the concept of a node set namely Maximum Quadruple-Constrained subset MQC(A,a - e) to improve the efficiency of exact inference in diagnostic Bayesian networks (DBNs). Here, A denotes a node set in a DBN and a - e represent five real numbers. The improvement in efficiency is achieved by computation sharing. That is, we divide inference in a DBN into the computation of eliminating MQC(A, a - e) and the subsequent computation. For certain complex DBNs and (A, a - e), the former computation covers a major part of the whole computation, and the latter one is highly efficient after sharing the former computation. Searching for MQC(A, a - e) is a combinatorial optimization problem. A backtracking-based exact algorithm Backtracking-Search (BS) was proposed, however the time complexity of BS is O(n(3)2(n)) (n = vertical bar A vertical bar). In this article, we propose the following algorithms for searching for MQC(A, a - e) especially in complex DBNs where vertical bar A vertical bar is large. (i) A divide-and-conquer algorithm Divide-and-Conquer (DC) for dividing the problem of searching for MQC(A, a - e) into sub-problems of searching for MQC(B-1, a - e), ...,MQC(B-m, a - e), where B-i subset of A(1 <= i m, 1 <= m <= vertical bar A vertical bar). (ii) A DC-based heuristic algorithm Heuristic-Search (HS) for searching for MQC(B-i, a - e). The time complexity of HS is O(n(6)) (n = vertical bar B-i vertical bar). Empirical results show that, HS outperforms BS over a range of networks.(C) 2011 Elsevier B.V. All rights reserved.

关键词： Diagnostic Bayesian networks Exact inference computation sharing Inference simplifying Search

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High-performance FIR filter design based on sharing multiplication

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IEEE TRANSACTIONS ON VERY LARGE SCALE INTEGRATION (VLSI) SYSTEMS 2003年第2期11卷 244-253页

作者： Park, J Muhammad, K Roy, K Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Texas Instruments Inc Dallas TX 75243 USA

Finite impulse response (FIR) filtering can be expressed as multiplications of vectors by scalars. We present high-speed designs for FIR filters based on a computation sharing multiplier which specifically targets computation re-use in vector-scalar products. The performance of the proposed implementation is compared with implementations based on carry-save and Wallace tree multipliers in 0.35-mum technology. We show that sharing multiplier scheme improves speed by approximately 52 and 33% with respect to the FIR filter implementations based on the carry-save multiplier and Wallace tree multiplier, respectively. In addition, sharing multiplier scheme has a relatively, small power delay product than other multiplier schemes. Using voltage scaling, power consumption of the FIR filter based on computation sharing multiplier can be reduced to 41% of the FIR filter based on the Wallace tree multiplier for the same frequency of operation.

关键词： computation sharing computation sharing multiplier high-performance finite impulse response (FIR) filter

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High performance and low power FIR filter design based on sharing multiplication

High performance and low power FIR filter design based on sh...

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International Symposium on Low Power Electronics and Design

作者： Park, J Jeong, WY Choo, H Mahmoodi-Meimand, H Wang, YT Roy, K Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47906 USA

ISBN: (纸本)1581134754

We present a high performance and low power FIR filter design, which is based on computation sharing multiplier (CSHM). CSHM specifically targets computation re-use in vector-scalar products and is effectively used in our FIR filter design. Efficient circuit level techniques: a new carry select adder and conditional capture flip-flop (CCFF), are also used to further improve power and performance. The proposed FIR filter architecture was implemented in 0.25 mum technology. Experimental results on a 10 tap low pass CSHM FIR filter show speed and power improvement of 19% and 17%, respectively, with respect to an FIR filter based on Wallace tree multiplier.

关键词： computation sharing FIR filter design high performance and low power carry select adder conditional capture flip-flop

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On-line Sensing Task Optimization for Shared Sensors

On-line Sensing Task Optimization for Shared Sensors

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9th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)

作者： Tavakoli, Arsalan Kansal, Aman Nath, Suman Univ Calif Berkeley Berkeley CA 94720 USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781605589886

Shared sensing infrastructures that allow multiple applications to share deployed sensors are emerging and Internet protocol based access for such sensors has already been prototyped and deployed. As a large number of applications start accessing shared sensors, the efficiency of resource usage at the embedded nodes and in the network infrastructure supporting them becomes a concern. To address this, we develop methods that detect when common data and common stream processing is requested by multiple applications, including cases where only some of the data is shared or only intermediate processing steps are common. The communication and processing is then modified to eliminate the redundancies. Specifically, we use an interval-cover graph to minimize communication redundancies and a joint data flow graph optimization to remove computational redundancies. Both methods operate online and allow application requests to be dynamically added or removed. The proposed methods are evaluated using applications on a road traffic sensing infrastructure.

关键词： wireless sensor networks multi-query optimization computation sharing

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Gloria: Graph-based sharing Optimizer for Event Trend Aggregation 22

Gloria: Graph-based Sharing Optimizer for Event Trend Aggreg...

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International Conference on Management of Data (SIGMOD)

作者： Ma, Lei Lei, Chuan Poppe, Olga Rundensteiner, Elke A. Worcester Polytech Inst 100 Inst Rd Worcester MA 01609 USA Instacart 50 Beale St Suite 600 San Francisco CA 94105 USA Microsoft Gray Syst Lab One Microsoft Way Redmond WA 98052 USA

ISBN: (纸本)9781450392495

Large workloads of event trend aggregation queries are widely deployed to derive high-level insights about current event trends in near real time. To speed-up the execution, we identify and leverage sharing opportunities from complex patterns with flat Kleene operators or even nested Kleene expressions. We propose GLORIA, a graph-based sharing optimizer for event trend aggregation. First, we map the sharing optimization problem to a graph path search problem in the GLORIA graph with execution costs encoded as weights. Second, we shrink the search space by applying cost-driven pruning principles that guarantee optimality of the reduced GLORIA graph in most cases. Lastly, we propose a path search algorithm that identifies the sharing plan with minimum execution costs. Our experimental study on three real-world data sets demonstrates that our GLORIA optimizer effectively reduces the search space, leading to 5-fold speed-up in optimization time. The optimized plan consistently reduces the query latency by 68% 93% compared to the plan generated by state-of-the-art approaches.

关键词： Complex event processing query optimization computation sharing incremental aggregation event trend

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To Share, or not to Share Online Event Trend Aggregation Over Bursty Event Streams 21

To Share, or not to Share Online Event Trend Aggregation Ove...

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ACM SIGMOD International Conference on Management of Data (SIGMOD)

作者： Poppe, Olga Lei, Chuan Ma, Lei Rozet, Allison Rundensteiner, Elke A. Microsoft Gray Syst Lab One Microsoft Way Redmond WA 98052 USA IBM Res Almaden 650 Harry Rd San Jose CA 95120 USA Worcester Polytech Inst 100 Inst Rd Worcester MA 01609 USA MathWorks 1 Apple Hill Dr Natick MA 01760 USA

ISBN: (纸本)9781450383431

Complex event processing (CEP) systems continuously evaluate large workloads of pattern queries under tight time constraints. Event trend aggregation queries with Kleene patterns are commonly used to retrieve summarized insights about the recent trends in event streams. State-of-art methods are limited either due to repetitive computations or unnecessary trend construction. Existing shared approaches are guided by statically selected and hence rigid sharing plans that are often sub-optimal under stream fluctuations. In this work, we propose a novel framework HAMLET that is the first to overcome these limitations. HAMLET introduces two key innovations. First, HAMLET adaptively decides at run time whether to share or not to share computations depending on the current stream properties to harvest the maximum sharing benefit. Second, HAMLET is equipped with a highly efficient shared trend aggregation strategy that avoids trend construction. Our experimental study on both real and synthetic data sets demonstrates that HAMLET consistently reduces query latency by up to five orders of magnitude compared to state-of-the-art approaches.

关键词： Complex Event Processing query optimization computation sharing incremental aggregation event trend

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GEDS: GPU execution of spatio-temporal queries over spatio-temporal data streams

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Journal of Embedded Computing 2011年第3,4期4卷 117-130页

作者： Cazalas, Jonathan Guha, Ratan School of Electrical Engineering and Computer Science University of Central Florida Orlando FL USA

Much research exists for the efficient processing of spatio-temporal data streams. However, all methods ultimately rely on an ill-equipped processor [22], namely a CPU, to evaluate concurrent, continuous spatio-temporal queries over these data streams. This paper presents GEDS, a scalable, Graphics Processing Unit (GPU)-based framework for the evaluation of continuous queries over spatio-temporal data streams. Specifically, GEDS employs the computation sharing and parallel processing paradigms to deliver scalability in the evaluation of continuous, spatio-temporal range queries and continuous, spatio-temporal kNN queries. The GEDS framework utilizes the parallel processing capability of the GPU, a stream processor by trade, to handle the computation required in this application. Experimental evaluation shows promising performance and shows the scalability and efficacy of GEDS in spatio-temporal data streaming environments. Additional performance studies demonstrate that, even in light of the costs associated with memory transfers, the parallel processing power provided by GEDS clearly counters and outweighs any associated costs.

关键词： Spatio-temporal data streams computation sharing parallel processing location-based services mobile database systems continuous query range query kNN graphical processing unit GPU

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