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arXiv 2024年

作者： Yu, Chin-Yun Pauwels, Johan Fazekas, György Centre for Digital Music Queen Mary University of London United Kingdom

In binaural audio synthesis, aligning head-related impulse responses (HRIRs) in time has been an important pre-processing step, enabling accurate spatial interpolation and efficient data compression. The maximum correlation time delay between spatially nearby HRIRs has previously been used to get accurate and smooth alignment by solving a matrix equation in which the solution has the minimum Euclidean distance to the time delay. However, the Euclidean criterion could lead to an over-smoothing solution in practice. In this paper, we solve the smoothing issue by formulating the task as solving an integer linear programming problem equivalent to minimising an L1-norm. Moreover, we incorporate 1) the cross-correlation of inter-aural HRIRs, and 2) HRIRs with their minimum-phase responses to have more reference measurements for optimisation. We show the proposed method can get more accurate alignments than the Euclidean-based method by comparing the spectral reconstruction loss of time-aligned HRIRs using spherical harmonics representation on seven HRIRs consisting of human and dummy heads. The extra correlation features and the L1-norm are also beneficial in extremely noisy conditions. In addition, this method can be applied to phase unwrapping of head-related transfer functions, where the unwrapped phase could be a compact feature for downstream tasks. © 2024, CC BY.

关键词： integer linear programming

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CILP: An Arbitrary-bit Precision All-digital Compute-in-memory Solver for integer linear programming Problems

CILP: An Arbitrary-bit Precision All-digital Compute-in-memo...

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Custom Integrated Circuits Conference (CICC)

作者： Mengtian Yang Yipeng Wang Shanshan Xie Chieh-Pu Lo Meizhi Wang Sirish Oruganti Rishabh Sehgal Jaydeep P. Kulkarni The University of Texas at Austin Austin TX

ISBN: (数字)9798350394061

ISBN: (纸本)9798350394078

integer linear programming (ILP) is an NP-complete combinatorial optimization problem (COP), suggesting that it is computationally challenging to solve due to its exponentially increased operations with scaling. As shown in Fig. 1, The ILP is relevant in various real-world scenarios such as computational biology [1], investment decision, automated driving, and electronic design automation [2]. An ILP solver aims to find a set of integer variables $(x)$ to maximize a linear objective function $(c\cdot x)$ , subject to a set of linear constraints $(A\cdot x\leq b)$ . With the increasingly wide usage of ILP, various new solving algorithms [3] have been proposed, but the performance are limited by substantial memory access. ILP coefficients are fixed during solving, but software solvers on cache-register architectures frequently access cache to reload coefficients because of small register file size, causing up to a $10^{14}\mathrm{x}$ disparity between stored and accessed memory bits. FPGA [4] and AISC [5] accelerated solvers improve the speed by customized processing element (PE), but they still need frequent accesses to Block-RAM or scratch pad. Compute-in-memory (CIM) solutions are well-suited for ILP solving which has extremely high data reuse, but existing CIM DNN accelerators incur precision loss with hardware tradeoffs, which is unacceptable for ILP where the feasibility checking must be correct. Previous all-digital CIM COP solver for Boolean variables [6] uses a customized $6\mathrm{T}$ -6T{###} $3\mathrm{T}$ cell, limiting their adaptability to different technologies.

关键词： Microprocessors Software algorithms Computer architecture integer linear programming In-memory computing linear programming Common Information Model (computing)

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Fine-Grained Equivalence for Problems Related to integer linear programming

arXiv

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arXiv 2024年

作者： Rohwedder, Lars Wȩgrzycki, Karol Maastricht University Maastricht Netherlands Saarland University Max Planck Institute for Informatics Saarbrücken Germany

integer linear programming with n binary variables and m many 0/1- constraints can be solved in time 2 O (m2)poly(n) and it is open whether the dependence on m is optimal. Several seemingly unrelated problems, which in- clude variants of Closest String, Discrepancy Minimization, Set Cover, and Set Packing, can be modelled as integer linear programming with 0/1 constraints to obtain algorithms with the same running time for a natural parameter m in each of the problems. Our main result establishes through fine-grained re- ductions that these problems are equivalent, meaning that a 2O(m2-ϵ)poly(n) algorithm with ϵ > 0 for one of them implies such an algorithm for all of them. In the setting above, one can alternatively obtain an nO(m) time algo- rithm for integer linear programming using a straightforward dynamic pro- gramming approach, which can be more efficient if n is relatively small (e.g., subexponential in m). We show that this can be improved to n'O(m)+O(nm), where n' is the number of distinct (i.e., non-symmetric) variables. This dom- inates both of the aforementioned running times. © 2024, CC0.

关键词： integer linear programming

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Hybrid Quantum-Classical Computing via Dantzig-Wolfe Decomposition for integer linear programming

Hybrid Quantum-Classical Computing via Dantzig-Wolfe Decompo...

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International Conference on Computer Communications and Networks (ICCCN)

作者： Xinliang Wei Jiyao Liu Lei Fan Yuanxiong Guo Zhu Han Yu Wang Department of Computer and Information Sciences Temple University Philadelphia PA USA Department of Engineering Technology University of Houston Houston TX USA Department of Electrical and Computer Engineering University of Houston Houston TX USA Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio TX USA

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439

Numerous optimization scenarios such as industrial production planning, network communication routing, and logistic scheduling can be modeled as large-scale integer linear programming problems. However, due to the NP-Hardness of these problems, it is very challenging to optimally solve these problems in a short time on classical computers. Quantum computers have emerged as a new computing platform to provide new computing paradigms to tackle these problems. However, the scalability and efficiency of current quantum computers pose significant challenges in practical implementations of quantum optimization algorithms. In this paper, we propose a novel hybrid quantum-classical approach, termed Hybrid quantum-classical Dantzig-Wolfe Decomposition (HyDWD), aimed at solving these problems. In this framework, the subproblems can be solved in parallel on quantum computers. Our results demonstrate the benefits of integrating parallel quantum computing with the proposed hybrid quantum-classical framework via Dantzig-Wolfe decomposition, paving the way for advancements in optimization and decision-making processes.

关键词： Computers Quantum computing Computational modeling Scalability Simulated annealing integer linear programming Quantum annealing

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Novel Genomic Duplication Models through integer linear programming 21

Novel Genomic Duplication Models through Integer Linear Prog...

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12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Paszek, Jaroslaw Eulenstein, Oliver Gorecki, Pawel Univ Warsaw Fac Math Informat & Mech Warsaw Poland Iowa State Univ Dept Comp Sci Ames IA USA

ISBN: (纸本)9781450384506

Unveiling ancient whole-genome duplications, or WGDs, in the evolutionary history of species is elementary to understand how gene families have formed over time and genomes evolved. A classic framework of WGD models for deciphering ancient species in which genome duplications occurred is based on reconciling multiple gene trees with a species tree. Reconciling gene trees with a species tree reveals evolutionary scenarios describing how genes have evolved along species tree branches through speciation and single duplication events. Clustering single duplication events from different gene trees occurring in the same species can reveal duplication episodes indicative of remnants of ancient WGDs. WGD models can be categorized into restricted and unrestricted models. Restricted models only consider scenarios where single duplications are limited by the timing of their ancestor speciation, while unrestricted models consider all possible evolutionary scenarios. Representing two extremes of the overall spectrum of possible scenarios, unconstrained models are biased towards locating duplication episodes close to the root of the species tree, while the constrained models tend to locate episodes close to the most recent species that theoretically could have contained them. Adding flexibility for improved biological realism, in this work, we develop and analyze a novel framework of WGD models encompassing the whole range of intermediate locations by defining, implementing, and testing models under multiple constraint strategies. We achieve this by formulating the first ILP model for the NP-hard problem of computing duplication episodes under the classic unrestricted WGD model from Fellows et al. and then incorporating constraints into this formulation reflecting WGD models for intermediate locations. Finally, we demonstrate the exemplary performance of our models and that our ILP formulations allow computing typical problem instances occurring in practice.

关键词： genomic duplication reconciliation duplication clustering whole genome duplication minimum episodes integer linear programming

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Formulating Data-arrival Synchronizers in integer linear programming for CGRA Mapping 58

Formulating Data-arrival Synchronizers in Integer Linear Pro...

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58th ACM/IEEE Design Automation Conference (DAC)

作者： Guo, Yijiang Wang, Jiarui Zhang, Jiaxi Luo, Guojie Peking Univ Ctr Energy Efficient Comp & Applicat Beijing Peoples R China Peng Cheng Lab Shenzhen Guangdong Peoples R China

ISBN: (纸本)9781665432740

Coarse-grained reconfigurable architecture (CGRA) is a promising programmable device with high performance and power efficiency. The CGRA compilation problem is to map an application onto a 3D time-space model of CGRA. Adding the circuitry of synchronizers can relax the mapping constraint for data alignment in time;and thus, it significantly influences the compilation performance. However, data-departure synchronizers may be infeasible, because an output value may be used multiple times and has a high fan-out. Instead, data-arrival synchronizers can further improve performance and mappability of CGRAs with acceptable overhead, compared to the synchronization methods based on detour routing, register files, and FIFO. In this work, we design two kinds of data-arrival synchronizers and formulate them in an integer linear programming (ILP) based mapping approach. The separate ILP formulations of placement and routing speed up the architecture exploration with synchronizers by up to 3.03x. The experimental study shows that data-arrival synchronizers improve CGRA performance by 19.8% on average. The results of our quantitative study show that synchronizers also improve the mapping success rate by 1.91x on average. In conclusion, CGRAs with appropriate synchronizers have better mappability while using fewer resources.

关键词： Performance evaluation Solid modeling Three-dimensional displays Design automation integer linear programming Routing Registers

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GOMIL: Global Optimization of Multiplier by integer linear programming

GOMIL: Global Optimization of Multiplier by Integer Linear P...

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Design, Automation and Test in Europe Conference and Exhibition (DATE)

作者： Xiao, Weihua Qian, Weikang Liu, Weiqiang Shanghai Jiao Tong Univ Univ Michigan Shanghai Jiao Tong Univ Joint Inst Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China Fudan Univ State Key Lab ASIC & Syst Shanghai Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing Peoples R China

ISBN: (纸本)9783981926354

Multiplier is an important arithmetic circuit. State-of-the-art designs consist of a partial product generator (PPG), a compressor tree (CT), and a carry propagation adder (CPA), with the last two components dominating the area and delay. Existing representative works optimize the CT and the CPA separately, adding a rigid boundary between these two components. In this paper, we break the boundary by proposing GOMIL, a global optimization for multiplier by integer linear programming. Two ILP sub-problems are first formulated to optimize the CT and the prefix structure in the CPA, respectively. Then, they are unified to provide a global optimization to the multiplier. The proposed method is applicable to not only multipliers with the AND gate-based PPG, but also those with Booth encoding-based PPG. The experimental results showed that the multipliers optimized by GOMIL can reduce the power-delay product by up to 71%, compared to the state-of-the-art multipliers developed in industry. The code of GOMIL is made open-source.

关键词： Multiplier Compressor Tree Prefix Tree integer linear programming Optimization

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Improving Search Efficiency and Diversity of Solutions in Multiobjective Binary Optimization by Using Metaheuristics Plus integer linear programming 24th

Improving Search Efficiency and Diversity of Solutions in Mu...

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24th International Conference on the Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Dominguez-Rios, Miguel Angel Chicano, Francisco Alba, Enrique Univ Malaga ITIS Software E-29071 Malaga Spain

ISBN: (纸本)9783030726980;9783030726997

Metaheuristics for solving multiobjective problems can provide an approximation of the Pareto front in a short time, but can also have difficulties finding feasible solutions in constrained problems. integer linear programming solvers, on the other hand, are good at finding feasible solutions, but they can require some time to find and guarantee the efficient solutions of the problem. In this work we combine these two ideas to propose a hybrid algorithm mixing an exploration heuristic for multiobjective optimization with integer linear programming to solve multiobjective problems with binary variables and linear constraints. The algorithm has been designed to provide an approximation of the Pareto front that is well-spread throughout the objective space. In order to check the performance, we compare it with three popular metaheuristics using two benchmarks of multiobjective binary constrained problems. The results show that the proposed approach provides better performance than the baseline algorithms in terms of number of the solutions, hypervolume, generational distance, inverted generational distance, and the additive epsilon indicator.

关键词： Multiobjective optimization Hybrid algorithms integer linear programming

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How to Optimize Joint Routing and Scheduling Models for TSN Using integer linear programming 2021

How to Optimize Joint Routing and Scheduling Models for TSN ...

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29th International Conference on Real-Time Networks and Systems (RTNS)

作者： Hellmanns, David Haug, Lucas Hildebrand, Moritz Duerr, Frank Kehrer, Stephan Hummen, Rene Univ Stuttgart Inst Parallel & Distributed Syst Stuttgart Germany Hirschmann Automat & Control GmbH Technol & Innovat Dept Neckartenzlingen Germany

ISBN: (纸本)9781450390019

Reliable real-time communication is an essential technology for industrial manufacturing but also other branches to transport missioncritical messages. IEEE Time-Sensitive Networking (TSN) is a disruptive real-time communication standard extending IEEE Ethernet with real-time mechanisms. One of the core features of TSN is the Time-Aware Shaper (TAS) enabling TDMA-based scheduling of streams within the network. TDMA has many advantages from the real-time perspective. Foremost, stream isolation in the time dimension enables tight delay and jitter bounds. Moreover, conformance to these bounds is proven by the design of the TDMA schedule. However, calculating an optimal schedule is an NP-hard problem. Therefore, various approaches to optimize the schedule calculation are proposed, such as integer linear programming (ILP). Nevertheless, a systematic comparsion of the different optimization approaches with respect to their performance is missing so far. To fill this gap, we first provide a systematic classification of optimizations of ILP-based TSN scheduling. To quantify the effects of such optimization approaches, we introduce a base ILP and propose optimizations for the different categories. Using the proposed optimization, we evaluate the performance with regard to execution time and schedulability (number of solved schedules). Our results show that the optimizations lead to strongly fluctuating results. Certain intuitive optimizations can even lead to massive performance degradations.

关键词： Time-Sensitive Networking TSN Scheduling Routing integer linear programming Optimization Model

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Hybrid geometry trimming algorithm based on integer linear programming for fluid flow topology optimization

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COMPUTERS & FLUIDS 2022年 244卷

作者： Moscatelli, Eduardo de Sa, Luis Fernando Nogueira Ranjbarzadeh, Shahin Picelli, Renato Gioria, Rafael dos Santos Silva, Emilio Carlos Nelli Univ Sao Paulo Dept Mechatron & Mech Syst Engn Polytech Sch Sao Paulo SP Brazil Univ Dept Min & Petr Engn Polytech Sch Sao Paulo SP Brazil

In this work, a hybrid geometry trimming algorithm based on Topology Optimization of Binary Structures (TOBS) is proposed to design fluid flow devices with reduced seepage. The problem of solid reintroduction during optimization is solved by combining the geometry trimmed functional with the extended porous domain functional in a multi-objective function. The method is evaluated in the topology optimization of fixed-geometry fluidic diodes, which is a class of problems known to present difficulties related to high fluid penetration in the porous regions. Also, a new convergence criterion for TOBS is proposed to eliminate the necessity of selecting a convergence tolerance and to avoid running the optimization for cycles of the same designs. Fluidic diodes are designed for several Reynolds numbers (Re) to prove the effectiveness of the proposed formulation. The forward problem is solved by the Finite Element Method (FEM), and the sensitivities are calculated via automatic differentiation. The optimization problem is solved by integer linear programming.

关键词： Topology optimization TOBS integer linear programming geometry trimming fluidic diodes Finite Element Method

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