Symmetric Tori connected Torus Network

A Symmetric Tori connected Torus Network (STTN) is a 2D-torus network of multiple basic modules, in which the basic modules are 2D-torus networks that are hierarchically interconnected for higher-level networks. In this paper, we present the architecture of the STTN, addressing of node, routing of message, and evaluate the static network performance of STTN, TTN, TESH, mesh, and torus networks. It is shown that the STTN possesses several attractive features, including constant degree, small diameter, low cost, small average distance, moderate bisection width, and high fault tolerant performance than that of other conventional and hierarchical interconnection networks

Hierarchical 3D-Mesh Network

A Hierarchical 3D-Mesh (H3DM) Network is a 2D-mesh network of multiple basic modules, in which the basic modules are 3D-torus networks that are hierarchically interconnected for higher-level networks. In this paper, we evaluate the dynamic communication performance of a H3DM network using a deadlock-free routing algorithm with minimum number of virtual channels under the uniform and non-uniform traffic patterns; and compare it with other networks to show the superiority of the H3DM network over other networks. We have also evaluated the dynamic communication performance of the mesh and torus networks. It is shown that H3DM network yields low average transfer time than that of mesh and torus networks. The trade-off between throughput and latency of these networks shown that H3DM network provide better dynamic communication performance than that of mesh and torus networks before saturation

High performance hierarchical torus network

A Hierarchical Torus Network (HTN) is a 2D-torus of multiple basic modules, in which the basic modules are 3D-torus that are hierarchically interconnected for higher level networks. This research addresses the architecture of the HTN, deadlock-free dimension-order and adaptive routing algorithms, static network performance, and dynamic communication performance (DCP). The static network performances are derived from the graph model and the DCP is evaluated by using dimension-order routing and newly proposed adaptive routing algorithms under various traffic patterns. It is shown that the HTN possesses several attractive features, including constant node degree, small diameter, low cost, small average distance, moderate (neither too low, nor too high) bisection width, and high throughput and low latency, which provide better DCP than that of other conventional and hierarchical interconnection networks. It is also found that the DCP of an HTN using the proposed adaptive routing algorithms are better than when the dimension-order routing is used, in terms of network throughput

Power analysis with variable traffic loads for next generation interconnection networks

Power consumption is the most important factor for the consideration of next generation supercomputers. In addition, the requirement of power usages can be even scaled up to more than 300MW (which is nearly equal to the one nuclear power plant) with the conventional networks. On the other hand, hierarchical interconnection networks can be a possible solution to those issues. 3D-TTN is a hierarchical interconnection network where lowest level is configured as the 3Dtorus network, following the 2Dtorus network at the higher-level networks. The main focus for this paper is the power analysis with variable traffic load along with the fault tolerance, cost, packing density and message traffic density of 3D-TTN comparing against various other networks. In our early research, 3D-TTN has achieved near about 21% better diameter performance, 12% better average distance performance and eventually required about 32.48% less router power usage at the lowest level than the 5Dtorus network for 1% traffic load. This paper shows the power comparison with the router and link power rather than considering the router power only. Our analysis shows that 3DTTN will require about 39.96% less router and link power than the 5Dtorus network for 10% traffic. With 30% traffic load, 3DTTN will require about 38.42% less power than the 5Dtorus network for the on-chip network. Even considering some topological parameters, 3D-TTN could also achieve some desirable performance by comparing other networks

Convolutional neural network training with artificial pattern for Bangla handwritten numeral recognition

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high dimensional complex task and existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this study, a CNN based method has been investigated for Bangla handwritten numeral recognition. A moderated pre-processing has been adopted to produce patterns from handwritten scan images. On the other hand, CNN has been trained with the patterns plus a number of artificial patterns. A simple rotation based approach is employed to generate artificial patterns. The proposed CNN with artificial pattern is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset

The performance evaluation of a 3D torus network using partial link-sharing method in NoC router buffer

The high performance network-on-chip (NoC) router using minimal hardware resources to minimize the layout area is very essential for NoC design. In this paper, we have proposed a memory sharing method of a wormhole routed NoC architecture to alleviate the area overhead of a NoC router. In the proposed method, a memory is shared by multiple physical links by using a multi-port memory. In this paper, we have proposed a partial link-sharing method and evaluated the communication performance using the proposed method. It is revealed that the resulted communication performance by the proposed methods is higher than that of the conventional method, and the progress ratio of the 3D-torus network is higher than that of 2D-torus network. It is shown that the improvement of communication performance using partial link sharing method is achieved with slightly increase of hardware cost. Copyright © 2017 The Institute of Electronics, Information and Communication Engineers

Multiple convolutional neural network training for Bangla handwritten numeral recognition

Recognition of handwritten numerals has gained much interest in recent years due to its various application potentials. The progress of handwritten Bangla numeral is well behind Roman, Chinese and Arabic scripts although it is a major language in Indian subcontinent and is the first language of Bangladesh. Handwritten numeral classification is a high-dimensional complex task and existing methods use distinct feature extraction techniques and various classification tools in their recognition schemes. Recently, convolutional neural network (CNN) is found efficient for image classification with its distinct features. In this study, three different CNNs with same architecture are trained with different training sets and combined their decisions for Bangla handwritten numeral recognition. One CNN is trained with ordinary training set prepared from handwritten scan images; and training sets for other two CNNs are prepared with fixed (positive and negative, respectively) rotational angles of original images. The proposed multiple CNN based approach is shown to outperform other existing methods while tested on a popular Bangla benchmark handwritten dataset

The proposal of partial sharing for link-sharing method of buffer in NoC router

The memory is shared between multiple physical links by using the multi-port memory in the link sharing method of the wormhole routed network-on-chip architecture. By this link sharing method the communication performance is substantially improved with extra hardware cost. In this paper, we propose a partial sharing method of a memory by two physical links. It is shown that this limited capacity of memory sharing in this proposed partial sharing method significantly reduces the hardware cost with slight declination of hardware cost