Assessing the Suitability of King Topologies for Interconnection Networks

In the late years many different interconnection networks have been used with two main tendencies. One is characterized by the use of high-degree routers with long wires while the other uses routers of much smaller degree. The latter rely on two-dimensional mesh and torus topologies with shorter local links. This paper focuses on doubling the degree of common 2D meshes and tori while still preserving an attractive layout for VLSI design. By adding a set of diagonal links in one direction, diagonal networks are obtained. By adding a second set of links, networks of degree eight are built, named king networks. This research presents a comprehensive study of these networks which includes a topological analysis, the proposal of appropriate routing procedures and an empirical evaluation. King networks exhibit a number of attractive characteristics which translate to reduced execution times of parallel applications. For example, the execution times NPB suite are reduced up to a 30 percent. In addition, this work reveals other properties of king networks such as perfect partitioning that deserves further attention for its convenient exploitation in forthcoming high-performance parallel systems

Droplet routing for digital microfluidic biochips based on microelectrode dot array architecture

A digital microfluidic biochip (DMFB) is a device that digitizes fluidic samples into tiny droplets and operates chemical processes on a single chip. Movement control of droplets can be realized by using electrowetting-on-dielectric (EWOD) technology. DMFBs have high configurability, high sensitivity, low cost and reduced human error as well as a promising future in the applications of point-of-care medical diagnostic, and DNA sequencing. As the demands of scalability, configurability and portability increase, a new DMFB architecture called Microelectrode Dot Array (MEDA) has been introduced recently to allow configurable electrodes shape and more precise control of droplets. The objective of this work is to investigate a routing algorithm which can not only handle the routing problem for traditional DMFBs, but also be able to route different sizes of droplets and incorporate diagonal movements for MEDA. The proposed droplet routing algorithm is based on 3D-A* search algorithm. The simulation results show that the proposed algorithm can reduce the maximum latest arrival time, average latest arrival time and total number of used cells. By enabling channel-based routing in MEDA, the equivalent total number of used cells can be significantly reduced. Compared to all existing algorithms, the proposed algorithm can achieve so far the least average latest arrival time

A Dynamic Feature Interaction Framework for Multi-task Visual Perception

Multi-task visual perception has a wide range of applications in scene understanding such as autonomous driving. In this work, we devise an efficient unified framework to solve multiple common perception tasks, including instance segmentation, semantic segmentation, monocular 3D detection, and depth estimation. Simply sharing the same visual feature representations for these tasks impairs the performance of tasks, while independent task-specific feature extractors lead to parameter redundancy and latency. Thus, we design two feature-merge branches to learn feature basis, which can be useful to, and thus shared by, multiple perception tasks. Then, each task takes the corresponding feature basis as the input of the prediction task head to fulfill a specific task. In particular, one feature merge branch is designed for instance-level recognition the other for dense predictions. To enhance inter-branch communication, the instance branch passes pixel-wise spatial information of each instance to the dense branch using efficient dynamic convolution weighting. Moreover, a simple but effective dynamic routing mechanism is proposed to isolate task-specific features and leverage common properties among tasks. Our proposed framework, termed D2BNet, demonstrates a unique approach to parameter-efficient predictions for multi-task perception. In addition, as tasks benefit from co-training with each other, our solution achieves on par results on partially labeled settings on nuScenes and outperforms previous works for 3D detection and depth estimation on the Cityscapes dataset with full supervision.Comment: Accepted by International Journal of Computer Vision. arXiv admin note: text overlap with arXiv:2011.0979

Robust beam splitter with fast quantum state transfer through a topological interface

The Su-Schrieffer-Heeger (SSH) model, commonly used for robust state transfers through topologically protected edge pumping, has been generalized and exploited to engineer diverse functional quantum devices. Here, we propose to realize a fast topological beam splitter based on a generalized SSH model by accelerating the quantum state transfer (QST) process essentially limited by adiabatic requirements. The scheme involves delicate orchestration of the instantaneous energy spectrum through exponential modulation of nearest neighbor coupling strengths and onsite energies, yielding a significantly accelerated beam splitting process. Due to properties of topological pumping and accelerated QST, the beam splitter exhibits strong robustness against parameter disorders and losses of system. In addition, the model demonstrates good scalability and can be extended to two-dimensional crossed-chain structures to realize a topological router with variable numbers of output ports. Our work provides practical prospects for fast and robust topological QST in feasible quantum devices in large-scale quantum information processing.Comment: To be published in Frontiers of Physic