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5th International Conference on Devices for Integrated Circuit, DevIC 2023

作者： Mahan, Vishwaraj Patel, Manish I. Institute of Technology Nirma University Ahmedabad Department of Electronics and Communication Engineering India

ISBN: (纸本)9798350347265

Recent technology advancements have put a lot of emphasis on the optimization of three main aspects-speed, power, and area. The speed of a digital circuit is always dependent upon clock frequency. The clock frequency of a digital circuit is predominantly influenced by the computational cost of the critical path. The critical path of the digital circuits that contain combinational and sequential elements like FIR, IIR Filters, Multipliers, Adders, Digital Signal Processors (DSPs), etc. can be optimized by rearranging the delay(s) of the critical path, which eventually reduces computational cost without trading with the functionality and input-output relation of the circuit. The term 'Retiming' is used to describe the technique of reorganizing the storage elements (registers) in a digital circuit while maintaining its functionality. Currently, python is becoming a choice for a large community of software developers, in the VLSI domain also it is gaining popularity as a scripting language. In this article, the python implementation of the retiming technique is proposed, which takes a data flow Graph (DFG) of a non-retimed Digital Circuit as an input to generate the optimized DFG with less critical path delay by Brute Force search, thereby improving overall performance. © 2023 IEEE.

关键词： data flow graphs

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Early results on deep unfolded conjugate gradient-based large-scale MIMO detection

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IET COMMUNICATIONS 2021年第3期15卷 435-444页

作者： Ahmed Ouameur, Messaoud Massicotte, Daniel Univ Quebec Trois Rivieres Chaire Rech Signaux & Intelligence Syst Hautes Pe Lab Signaux & Syst Integres Dept Elect & Comp Engn Trois Rivieres PQ Canada

Deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deep unfolded conjugate gradient (CG) architecture for large-scale multiple-input multiple-output detection. The proposed technique combines the advantages of a model-driven approach in readily incorporating domain knowledge and deep learning in effective parameters learning. The parameters are trained via backpropagation over a data flow graph inspired from the iterative conjugate gradient method. We derive the closed-form expressions for the gradients for parameters training and discuss early results on the performance in a statistically identical and independent distributed channel where the training overhead is considerably low. It is worth noting that the loss function is based on the residual error that is not an explicit function of the desired signal, which makes the proposed algorithm blind. As an initial framework, we will point to the inherent issues and future directions.

关键词： deep unfolded conjugate gradient-based large-scale MIMO detection data flow graphs distributed channel signal detection Communications computing communication systems Digital signal processing deep learning (artificial intelligence) wireless channels Combinatorial mathematics multiple-input multiple-output detection Signal detection iterative methods deep learning telecommunication computing data flow graph backpropagation model-driven approach Interpolation and function approximation (numerical analysis) Radio links and equipment conjugate gradient methods iterative conjugate gradient method deep unfolded conjugate gradient architecture MIMO communication

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Scheduling of recursive and dynamic data-flow graphs using stream rewriting 26

Scheduling of recursive and dynamic data-flow graphs using s...

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26th IEEE International Symposium on Computer Architecture and High Performance Computing Workshops, SBAC-PADW 2014

作者： Middendorf, Lars Haubelt, Christian University of Rostock Germany

ISBN: (纸本)9781479970148

data-flow graphs, consisting of processes (actors) and communication channels, provide an efficient model of computation for analysis and implementation of highly parallel applications. We propose a novel algorithm for scheduling a large number of data-flow actors and also recursively expandable subgraphs by encoding their state and dependencies as a token stream. The proposed execution model enables global resource sharing, dynamic instantiation of actors and provides lightweight lock-free synchronization. Hence, our approach is most useful for compute-intensive applications with frequently varying and unpredictable data rates. In addition, we present a balanced scheduling algorithm, which restricts the memory usage of dynamic and recursive data-flow graphs, while still maintaining enough concurrency to keep all execution units utilized. © 2014 IEEE.

关键词： data flow graphs

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Agile design of DCT circuit on soft CGRA

Agile design of DCT circuit on soft CGRA

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2021 International Conference on High Performance Computing and Communication, HPCCE 2021

作者： Li, Dongping Luo, Guojie Yan, Wei School of Software and Microelectronics Peking Univ./City Beijing 24 Jinyuan Road Daxing District Beijing100000 China Center for Energy-efficient Computing and Applications Peking Univ./City Beijing 5 Summer Palace Road Haidian District Beijing100000 China

ISBN: (数字)9781510652019

ISBN: (纸本)9781510652002

High-level synthesis technology is a technology for circuit design using high-level languages such as C, C++, or System C. This design method is suitable for algorithm engineers to implement calculation prototypes and is not suitable for hardware engineers to design circuits from the register transfer level. In addition, from the perspective of agile development and code reuse, C, C++, and System C are far from another hardware description language, Chisel. However, there are relatively few electronic design automation tools for Chisel, so we propose a circuit design flow based on CGRA hardware, in which Chisel designs the PE unit of CGRA. However, Chisel has few electronic design automation tools, so a circuit design flow based on CGRA is proposed. Dedicated data flow graph generation tools and typical coarse-grained reconfigurable arrays are implemented for this design flow. This article describes a design process based on CGRA and compares the design process with HLS through the DCT circuit design process. Through the experimental comparison of the two circuit implementation processes, it is found that, first of all, the correctness of the CGRA-based design process and the availability of data flow diagram generation tools. Secondly, from a design point of view, the use of Chisel will increase the overall development speed and increase the reusability of design files. Finally, from the perspective of toolchains, part of the tools in the new design process uses open-source toolchains, which reduces the cost of using toolchains and promotes the development of toolchains to a certain extent. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： data flow graphs

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Spanning Trees in Directed Square Cycles

SSRN

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SSRN 2025年

作者： Tanaka, Yuuho Faculty of Science and Technology Oita University Oita870-1192 Japan

We classify weakly connected spanning closed (WCSC) subgraphs of $\overrightarrow{C_n^2}$, the square of a directed $n$-vertex *** we show that every spanning tree of $\overrightarrow{C_n^2}$ is contained in a unique nontrivial WCSC subgraph of $\overrightarrow{C_n^2}$.As a result, we obtain a purely combinatorial derivation of the formula for the number of directed spanning trees of $\overrightarrow{C_n^2}$.Moreover, we obtain the formula for the number of directed spanning trees of $\overrightarrow{C_n^2}$, which is a Jacobsthal number. © 2025, The Authors. All rights reserved.

关键词： data flow graphs

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Survey: Tensorflow in Machine Learning

Survey: Tensorflow in Machine Learning

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2022 International Conference on Applications of Intelligent Computing in Engineering and Science, AICES 2022

作者： Ramchandani, Monica Khandare, Hrishikesh Singh, Priyanshi Rajak, Prince Suryawanshi, Nidhi Jangde, Anjali Sagar Arya, Laxmi Kumar, Prithviraj Sahu, Mridu National Institute of Technology Raipur India

Tensorflow(TF) is a large-scale ML system that can be used in many different situations. data-flow graphs are used by TF to define operations, shared state, and computations that change that state. It distributes the nodes of a data-flow graph across multiple machines in a cluster, as well as diverse processing units within a single system, such as multicore CPUs, general-purpose GPUs and Tensor Processing Units, which are custom-designed ASICs (TPUs). Developers may use TF to try out new training techniques and optimizations. It covers a broad applications range, which focus on inference and DNN training. It is utilised in production by several Google services, and it has since been released as an open-source project and is broadly used in ML research. We review Tensorflow and place it in the context of modern deep learning techniques and technologies in this paper. We go over the fundamental computational concepts and how they work. The distributed execution concept, as well as its programming interface as accompanying visualisation toolkit. The remainder of this paper will provide a comprehensive overview of Tensorflow and place it in the context of machine learning today © Published under licence by IOP Publishing Ltd.

关键词： data flow graphs

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On Searching and Querying Maximum Directed (k, )-Plex

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IEEE Transactions on Knowledge and data Engineering 2025年

作者： Gao, Shuohao Yu, Kaiqiang Liu, Shengxin Long, Cheng Zhou, Xun Harbin Institute of Technology Shenzhen China Nanyang Technological University Singapore

Finding cohesive subgraphs from a directed graph is a fundamental approach to analyze directed graph data. We consider a new model called directed (k,)-plex for a cohesive directed subgraph, which is generalized from the concept of k-plex that is only applicable to undirected graphs. Directed (k,)-plex (or DPlex) has the connection requirements on both inbound and outbound directions of each vertex inside, i.e., each vertex disconnects at most k vertices and is meanwhile not pointed to by at most vertices. In this paper, we study the maximum DPlex search problem which finds a DPlex with the most vertices. We formally prove the NP-hardness of the problem. We then design a heuristic algorithm called DPHeuris, which finds a DPlex with the size close to the maximum one and runs practically fast in polynomial time. Furthermore, we propose a branch-and-bound algorithm called DPBB to find the exact maximum DPlex and develop effective graph reduction strategies for boosting the empirical performance. We also consider the problem of querying personalized maximum DPlex, and design a new method called DPBBQ for the problem. Finally, we conduct extensive experiments on real directed graphs. The experimental results show that (1) our heuristic method can quickly find a near-optimal solution and (2) our branch-and-bound method runs up to six orders of magnitude faster than other baselines. © 1989-2012 IEEE.

关键词： data flow graphs

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CPT Manager: An Open Environment for Constrained Path-Based Testing 18

CPT Manager: An Open Environment for Constrained Path-Based ...

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18th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Klima, Matej Bures, Miroslav Holotik, Daniel Herczeg, Maximilian Miltner, Marek Zanocco, Chad FEE Czech Technical University in Prague Dept. of Computer Science Prague Czech Republic Stanford University Civil and Environmental Engineering StanfordCA United States The Czech Technical University in Prague FEE Dept. of Economics Management and Humanities Prague Czech Republic

ISBN: (纸本)9798331534677

Path-based Testing is a common technique to test System Under Test (SUT) processes. Generally, a directed graph that models a system's workflow is input to the test path generation process, as well as the selected test coverage criterion. Several algorithms are proposed in the literature that traverse the graph and facilitate the generation of test cases for the selected coverage criterion. However, a plain directed graph used for modeling SUT processes does not allow for the capture of real-life dependencies and constraints between actions in the tested processes, which might pose a limit in application of this technique. Therefore, we defined an extended model that allows the specification of constraints upon the graph's elements and a set of algorithms that allow the generation of the set of test cases that satisfy the given constraints with the edge coverage. Considering the fact that in path-based testing, there is no platform in which engineers and researchers can share SUT models to be further assembled into open datasets to test performance of evolved path-based testing MBT algorithms, especially for the given problem of test paths generation with the constraints, this paper presents a summary of the problem and a novel management for creation and management of SUT models with constraints that allows the generation of test paths as well as to serve as a platform for creation of such benchmark datasets. © 2025 IEEE.

关键词： data flow graphs

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General-purpose dataflow Model with Neuromorphic Primitives

arXiv

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arXiv 2024年

作者： Zhang, Weihao Du, Yu Li, Hongyi Ma, Songchen Zhao, Rong Tsinghua University Beijing China

Neuromorphic computing exhibits great potential to provide high-performance benefits in various applications beyond neural networks. However, a general-purpose program execution model that aligns with the features of neuromorphic computing is required to bridge the gap between program versatility and neuromorphic hardware efficiency. The dataflow model offers a potential solution, but it faces high graph complexity and incompatibility with neuromorphic hardware when dealing with control flow programs, which decreases the programmability and performance. Here, we present a dataflow model tailored for neuromorphic hardware, called neuromorphic dataflow, which provides a compact, concise, and neuromorphic-compatible program representation for control logic. The neuromorphic dataflow introduces "when" and "where" primitives, which restructure the view of control. The neuromorphic dataflow embeds these primitives in the dataflow schema with the plasticity inherited from the spiking algorithms. Our method enables the deployment of general-purpose programs on neuromorphic hardware with both programmability and plasticity, while fully utilizing the hardware’s potential. © 2024, CC BY.

关键词： data flow graphs

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Program Analysis via Multiple Context Free Language Reachability

arXiv

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arXiv 2024年

作者： Conrado, Giovanna Kobus Kjelstrøm, Adam Husted Pavlogiannis, Andreas van de Pol, Jaco Hong Kong University of Science and Technology Hong Kong Aarhus University Denmark

Context-free language (CFL) reachability is a standard approach in static analyses, where the analysis question (e.g., is there a dataflow from x to y?) is phrased as a language reachability problem on a graph G wrt a CFL L. However, CFLs lack the expressiveness needed for high analysis precision. On the other hand, common formalisms for context-sensitive languages are too expressive, in the sense that the corresponding reachability problem becomes undecidable. Are there useful context-sensitive language-reachability models for static analysis? In this paper, we introduce Multiple Context-Free Language (MCFL) reachability as an expressive yet tractable model for static program analysis. MCFLs form an infinite hierarchy of mildly context sensitive languages parameterized by a dimension d and a rank r. Larger d and r yield progressively more expressive MCFLs, offering tunable analysis precision. We showcase the utility of MCFL reachability by developing a family of MCFLs that approximate interleaved Dyck reachability, a common but undecidable static analysis problem. Given the increased expressiveness of MCFLs, one natural question pertains to their algorithmic complexity, i.e., how fast can MCFL reachability be computed? We show that the problem takes O(n2d+1) time on a graph of n nodes when r = 1, and O(nd (r +1) ) time when r > 1. Moreover, we show that when r = 1, even the simpler membership problem has a lower bound of n2d based on the Strong Exponential Time Hypothesis, while reachability for d = 1 has a lower bound of n3 based on the combinatorial Boolean Matrix Multiplication Hypothesis. Thus, for r = 1, our algorithm is optimal within a factor n for all levels of the hierarchy based on the dimension d (and fully optimal for d = 1). We implement our MCFL reachability algorithm and evaluate it by underapproximating interleaved Dyck reachability for a standard taint analysis for Android. When combined with existing overapproximate methods, MCFL reachability disc

关键词： data flow graphs

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