PSO-based Power-driven X-Routing Algorithm in semiconductor design for predictive intelligence of IoT applications

PSO-based Power-Driven X-Routing Algorithm in semiconductor design for predictive intelligence of IoT applications

Microexpression can manifest the real mood of humans, which has been widely concerned in clinical diagnosis and depression analysis. To solve the problem of missing discriminative spatiotemporal features in a small data set caused by the short duration and subtle movement changes of microexpression, we present a dual-stream spatiotemporal attention network (DSTAN) that integrates dual-stream spatiotemporal network and attention mechanism to capture the deformation features and spatiotemporal features of microexpression in the case of small samples. The Spatiotemporal networks in DSTAN are based on two lightweight networks, namely, the spatiotemporal appearance network (STAN) learning the appearance features from the microexpression sequences and the spatiotemporal motion network (STMN) learning the motion features from optical flow sequences. To focus on the discriminative motion areas of microexpression, we construct a novel attention mechanism for the spatial model of STAN and STMN, including a multiscale kernel spatial attention mechanism and global dual-pool channel attention mechanism. To obtain the importance of each frame in the microexpression sequence, we design a temporal attention mechanism for the temporal model of STAN and STMN to form spatiotemporal appearance network-attention (STAN-A) and spatiotemporal motion network-attention (STMN-A), which can adaptively perform dynamic feature refinement. Finally, the feature concatenate-SVM method is used to integrate STAN-A and STMN-A to a novel network, DSTAN. The extensive experiments on three small spontaneous microexpression data sets of SMIC, CASME, and CASME II demonstrate the proposed DSTAN can effectively cope with the recognition of microexpressions.

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Micro Expression Recognition via Dual-Stream Spatiotemporal Attention Network.

Global routing is an important link in very large scale integration (VLSI) design. As the best model of global routing, X-architecture Steiner minimal tree (XSMT) has a good performance in wire length optimization. XSMT belongs to non-Manhattan structural model, and its construction process cannot be completed in polynomial time, so the generation of XSMT is an NP hard problem. In this paper, an X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution (XSMT-MoDDE) is proposed. Firstly, an effective encoding strategy, a fitness function of XSMT, and an initialization strategy of population are proposed to record the structure of XSMT, evaluate the cost of XSMT and obtain better initial particles, respectively. Secondly, elite selection and cloning strategy, multiple mutation strategies, and adaptive learning factor strategy are presented to improve the search process of discrete differential evolution algorithm. Thirdly, an effective refining strategy is proposed to further improve the quality of the final Steiner tree. Finally, the results of the comparative experiments prove that XSMT-MoDDE can get the shortest wire length so far, and achieve a better optimization degree in the larger-scale problem.

X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution.

PurposeThis paper aims to present a case study of virtual-reality-based product demonstrations featuring items of furniture. The results will be of use in further design and development of virtual-reality-based product demonstration systems and could also support effective student learning.Design/methodology/approachA new method was introduced to guide the experiment by confirming orthogonal arrays. User interactions were then planned, and a furniture demonstration system was implemented. The experiment comprised two stages. In the evaluation stage, participants were invited to experience the virtual-reality (VR)-based furniture demonstration system and complete a user experience (UX) survey. Taguchi-style robust design methods were used to design orthogonal table experiments and planning and design operation methods were used to implement an experimental display system in order to obtain optimized combinations of control factors and levels. The second stage involved a confirmatory test for the optimized combinations. A pilot questionnaire was first applied to survey demonstration scenarios that are important to customers.FindingsThe author found in terms of furniture products, product interactive display through VR can achieve good user satisfaction through quality design planning. VR can better grasp the characteristics of products than paper catalogs and website catalogs. And VR can better grasp the characteristics of products than online videos. For “interactive inspection”, “function simulation”, “style customization” and “set-out customization” were the most valuable demonstration scenarios for customers. The results of the experiment confirmed that the “overall rating”, “hedonic appeal” and “practical quality” were the three most important optimized operating methods, constituting a benchmark of user satisfaction.Originality/valueThe author found that it is possible to design and build a VR-based furniture demonstration system with a good level of usability when a suitable quality design method is applied. The optimized user interaction indicators and implementation experience for the VR-based product demonstration presented in this study will be of use in further design and development of similar systems.

Exploring the effectiveness of VR-based product demonstrations featuring items of furniture

Social learning discrete Particle Swarm Optimization based two-stage X-routing for IC design under Intelligent Edge Computing architecture

The X-architecture Steiner Minimum Tree (XSMT) is the best connection model for multi-terminal nets in global routing algorithms under non-Manhattan structures, and it is an NP-hard problem. And the successful application of Particle Swarm Optimization (PSO) technique in this field also reflects its extraordinary optimization ability. Therefore, based on Social Learning Particle Swarm Optimization (SLPSO), this paper proposes an XSMT construction algorithm (called SLPSO-XSMT) that can effectively balance exploration and exploitation capabilities. In order to expand the learning range of particles, a novel SLPSO approach based on the learning mechanism of example pool is proposed, which is conductive to break through local extrema. Then the proposed mutation operator is integrated into the inertia component of SLPSO to enhance the exploration ability of the algorithm. At the same time, in order to maintain the exploitation ability, the proposed crossover operator is integrated into the individual cognition and social cognition of SLPSO. Experimental results show that compared with other Steiner tree construction algorithms, the proposed SLPSO-XSMT algorithm has better wirelength optimization capability and superior stability.

SLPSO-Based X-Architecture Steiner Minimum Tree Construction

An X-Architecture SMT Algorithm Based on Competitive Swarm Optimizer.

The construction of timing-driven Steiner minimum tree is a critical issue in VLSI routing design. Meanwhile, since the interconnection model of X-architecture can make full use of routing resources compared to the traditional Manhattan architecture, constructing a Timing-Driven X-architecture Steiner Minimum Tree (TDXSMT) is of great significance to improving routing performance. In this paper, an efficient algorithm based on Social Learning Multi-Objective Particle Swarm Optimization (SLMOPSO) is proposed to construct a TDXSMT with minimizing the maximum source-to-sink pathlength. An X-architecture Prim-Dijkstra model is presented to construct an initial Steiner tree which can optimize both the wirelength and the maximum source-to-sink pathlength. In order to find a better solution, an SLMOPSO method based on the nearest and best select strategy is presented to improve the global exploration capability of the algorithm. Besides, the mutation and crossover operators are utilized to achieve the discrete particle update process, thereby better solving the discrete TDXSMT problem. The experimental results indicate that the proposed algorithm has an excellent trade-off between the wirelength and maximum source-to-sink pathlength of the routing tree and can greatly optimize the timing delay.

Ruping Zhou

Papers

Social learning discrete Particle Swarm Optimization based two-stage X-routing for IC design under Intelligent Edge Computing architecture

SLPSO-Based X-Architecture Steiner Minimum Tree Construction

An X-Architecture SMT Algorithm Based on Competitive Swarm Optimizer.

Timing-Driven X-architecture Steiner Minimum Tree Construction Based on Social Learning Multi-Objective Particle Swarm Optimization