Content addressable memory project

A parameterized version of the tree processor was designed and tested (by simulation). The leaf processor design is 90 percent complete. We expect to complete and test a combination of tree and leaf cell designs in the next period. Work is proceeding on algorithms for the computer aided manufacturing (CAM), and once the design is complete we will begin simulating algorithms for large problems. The following topics are covered: (1) the practical implementation of content addressable memory; (2) design of a LEAF cell for the Rutgers CAM architecture; (3) a circuit design tool user's manual; and (4) design and analysis of efficient hierarchical interconnection networks

Design and Analysis of Optical Interconnection Networks for Parallel Computation.

In this doctoral research, we propose several novel protocols and topologies for the interconnection of massively parallel processors. These new technologies achieve considerable improvements in system performance and structure simplicity. Currently, synchronous protocols are used in optical TDM buses. The major disadvantage of a synchronous protocol is the waste of packet slots. To offset this inherent drawback of synchronous TDM, a pipelined asynchronous TDM optical bus is proposed. The simulation results show that the performance of the proposed bus is significantly better than that of known pipelined synchronous TDM optical buses. Practically, the computation power of the plain TDM protocol is limited. Various extensions must be added to the system. In this research, a new pipelined optical TDM bus for implementing a linear array parallel computer architecture is proposed. The switches on the receiving segment of the bus can be dynamically controlled, which make the system highly reconfigurable. To build large and scalable systems, we need new network architectures that are suitable for optical interconnections. A new kind of reconfigurable bus called segmented bus is introduced to achieve reduced structure simplicity and increased concurrency. We show that parallel architectures based on segmented buses are versatile by showing that it can simulate parallel communication patterns supported by a wide variety of networks with small slowdown factors. New kinds of interconnection networks, the hypernetworks, have been proposed recently. Compared with point-to-point networks, they allow for increased resource-sharing and communication bandwidth utilization, and they are especially suitable for optical interconnects. One way to derive a hypernetwork is by finding the dual of a point-to-point network. Hypercube Q\sb{n}, where n is the dimension, is a very popular point-to-point network. It is interesting to construct hypernetworks from the dual Q\sbsp{n}{*} of hypercube of Q\sb{n}. In this research, the properties of Q\sbsp{n}{*} are investigated and a set of fundamental data communication algorithms for Q\sbsp{n}{*} are presented. The results indicate that the Q\sbsp{n}{*} hypernetwork is a useful and promising interconnection structure for high-performance parallel and distributed computing systems

Efficient Mapping of Neural Network Models on a Class of Parallel Architectures.

This dissertation develops a formal and systematic methodology for efficient mapping of several contemporary artificial neural network (ANN) models on k-ary n-cube parallel architectures (KNC\u27s). We apply the general mapping to several important ANN models including feedforward ANN\u27s trained with backpropagation algorithm, radial basis function networks, cascade correlation learning, and adaptive resonance theory networks. Our approach utilizes a parallel task graph representing concurrent operations of the ANN model during training. The mapping of the ANN is performed in two steps. First, the parallel task graph of the ANN is mapped to a virtual KNC of compatible dimensionality. This involves decomposing each operation into its atomic tasks. Second, the dimensionality of the virtual KNC architecture is recursively reduced through a sequence of transformations until a desired metric is optimized. We refer to this process as folding the virtual architecture. The optimization criteria we consider in this dissertation are defined in terms of the iteration time of the algorithm on the folded architecture. If necessary, the mapping scheme may utilize a subset of the processors of a given KNC architecture if it results in the most efficient simulation. A unique feature of our mapping is that it systematically selects an appropriate degree of parallelism leading to a highly efficient realization of the ANN model on KNC architectures. A novel feature of our work is its ability to efficiently map unit-allocating ANN\u27s. These networks possess a dynamic structure which grows during training. We present a highly efficient scheme for simulating such networks on existing KNC parallel architectures. We assume an upper bound on size of the neural network We perform the folding such that the iteration time of the largest network is minimized. We show that our mapping leads to near-optimal simulation of smaller instances of the neural network. In addition, based on our mapping no data migration or task rescheduling is needed as the size of network grows

Introduction to Multiprocessor I/O Architecture

The computational performance of multiprocessors continues to improve by leaps and bounds, fueled in part by rapid improvements in processor and interconnection technology. I/O performance thus becomes ever more critical, to avoid becoming the bottleneck of system performance. In this paper we provide an introduction to I/O architectural issues in multiprocessors, with a focus on disk subsystems. While we discuss examples from actual architectures and provide pointers to interesting research in the literature, we do not attempt to provide a comprehensive survey. We concentrate on a study of the architectural design issues, and the effects of different design alternatives

Mesh-of-Trees Interconnection Network for an Explicitly Multi-Threaded Parallel Computer Architecture

As the multiple-decade long increase in clock rates starts to slow down, main-stream general-purpose processors evolve towards single-chip parallel processing. On-chip interconnection networks are essential components of such machines, supporting the communication between processors and the memory system. This task is especially challenging for some easy-to-program parallel computers, which are designed with performance-demanding memory systems. This study proposes an interconnection network, with a novel implementation of the Mesh-of-Trees (MoT) topology. The MoT network is evaluated relative to metrics such as wire area complexity, total register count, bandwidth, network diameter, single switch delay, maximum throughput per area, trade-offs between throughput and latency, and post-layout performance. It is also compared with some other traditional network topologies, such as mesh, ring, hypercube, butterfly, fat trees, butterfly fat trees, and replicated butterfly networks. Concrete results show that MoT provides higher throughput and lower latency especially when the input traffic (or the on-chip parallelism) is high, at comparable area cost. The layout of MoT network is evaluated using standard cell design methodology. A prototype chip with 8-terminal MoT network was taped out at $90nm$ technology and tested. In the context of an easy-to-program single-chip parallel processor, MoT network is embedded in the eXplicit Multi-Threading (XMT) architecture, and evaluated by running parallel applications. In addition to the basic MoT architecture, a novel hybrid extension of MoT is proposed, which allows significant area savings with a small reduction in throughput

Design of an FPGA-based parallel SIMD machine for power flow analysis

Power flow analysis consists of computationally intensive calculations on large matrices, consumes several hours of computational time, and has shown the need for the implementation of application-specific parallel machines. The potential of Single-Instruction stream Multiple-Data stream (SIMD) parallel architectures for efficient operations on large matrices has been demonstrated as seen in the case of many existing supercomputers. The unsuitability of existing parallel machines for low-cost power system applications, their long design cycles, and the difficulty in using them show the need for application-specific SIMI) machines. Advances in VLSI technology and Field-Programmable Gate-Arrays (FPGAs) enable the implementation of Custom Computing Machines (CCMs) which can yield better performance for specific applications. The advent of SoftCore processors made it possible to integrate reconfigurable logic as a slave to a peripheral bus and has demonstrated the ability in the rapid prototyping of complete systems on programmable chips. This thesis aims at designing and implementing an FPGA-based SIMI) machine for power flow analysis. It presents the architecture of an SIMI) machine that consists of an array of processing elements with mesh interconnection and a Soft-Core processor; the latter is used as the host. The FPGAbased SIMI) machine is implemented on the Annapolis Microsystems Wildstar-II board that contains multiple Virtex-II FPGAs. The Soft-Core processor used is the Xilinx Microblaze and the application targeted is matrix multiplication

Three Highly Parallel Computer Architectures and Their Suitability for Three Representative Artificial Intelligence Problems

Virtually all current Artificial Intelligence (AI) applications are designed to run on sequential (von Neumann) computer architectures. As a result, current systems do not scale up. As knowledge is added to these systems, a point is reached where their performance quickly degrades. The performance of a von Neumann machine is limited by the bandwidth between memory and processor (the von Neumann bottleneck). The bottleneck is avoided by distributing the processing power across the memory of the computer. In this scheme the memory becomes the processor (a smart memory ). This paper highlights the relationship between three representative AI application domains, namely knowledge representation, rule-based expert systems, and vision, and their parallel hardware realizations. Three machines, covering a wide range of fundamental properties of parallel processors, namely module granularity, concurrency control, and communication geometry, are reviewed: the Connection Machine (a fine-grained SIMD hypercube), DADO (a medium-grained MIMD/SIMD/MSIMD tree-machine), and the Butterfly (a coarse-grained MIMD Butterflyswitch machine)

Efficient parallel processing with optical interconnections

With the advances in VLSI technology, it is now possible to build chips which can each contain thousands of processors. The efficiency of such chips in executing parallel algorithms heavily depends on the interconnection topology of the processors. It is not possible to build a fully interconnected network of processors with constant fan-in/fan-out using electrical interconnections. Free space optics is a remedy to this limitation. Qualities exclusive to the optical medium are its ability to be directed for propagation in free space and the property that optical channels can cross in space without any interference. In this thesis, we present an electro-optical interconnected architecture named Optical Reconfigurable Mesh (ORM). It is based on an existing optical model of computation. There are two layers in the architecture. The processing layer is a reconfigurable mesh and the deflecting layer contains optical devices to deflect light beams. ORM provides three types of communication mechanisms. The first is for arbitrary planar connections among sets of locally connected processors using the reconfigurable mesh. The second is for arbitrary connections among N of the processors using the electrical buses on the processing layer and N2 fixed passive deflecting units on the deflection layer. The third is for arbitrary connections among any of the N2 processors using the N2 mechanically reconfigurable deflectors in the deflection layer. The third type of communication mechanisms is significantly slower than the other two. Therefore, it is desirable to avoid reconfiguring this type of communication during the execution of the algorithms. Instead, the optical reconfiguration can be done before the execution of each algorithm begins. Determining a right configuration that would be suitable for the entire configuration of a task execution is studied in this thesis. The basic data movements for each of the mechanisms are studied. Finally, to show the power of ORM, we use all three types of communication mechanisms in the first O(logN) time algorithm for finding the convex hulls of all figures in an N x N binary image presented in this thesis

Combinatorial Design and Analysis of Optimal Multiple Bus Systems for Parallel Algorithms.

This dissertation develops a formal and systematic methodology for designing optimal, synchronous multiple bus systems (MBSs) realizing given (classes of) parallel algorithms. Our approach utilizes graph and group theoretic concepts to develop the necessary model and procedural tools. By partitioning the vertex set of the graphical representation CFG of the algorithm, we extract a set of interconnection functions that represents the interprocessor communication requirement of the algorithm. We prove that the optimal partitioning problem is NP-Hard. However, we show how to obtain polynomial time solutions by exploiting certain regularities present in many well-behaved parallel algorithms. The extracted set of interconnection functions is represented by an edge colored, directed graph called interconnection function graph (IFG). We show that the problem of constructing an optimal MBS to realize an IFG is NP-Hard. We show important special cases where polynomial time solutions exist. In particular, we prove that polynomial time solutions exist when the IFG is vertex symmetric. This is the case of interest for the vast majority of important interconnection function sets, whether extracted from algorithms or correspond to existing interconnection networks. We show that an IFG is vertex symmetric if and only if it is the Cayley color graph of a finite group $\Gamma$ and its generating set $\Delta.$ Using this property, we present a particular scheme to construct a symmetric $MBS\ M(\Gamma,\Delta)$ with minimum number of buses as well as minimum number of interfaces realizing a vertex symmetric IFG. We demonstrate several advantages of the optimal $MBS\ M(\Gamma,\Delta)$ in terms of its symmetry, number of ports per processor, number of neighbors per processor, and the diameter. We also investigate the fault tolerant capabilities and performance degradation of $M(\Gamma,\Delta)$ in the case of a single bus failure, single driver failure, single receiver failure, and single processor failure. Further, we address the problem of designing an optimal MBS realizing a class of algorithms when the number of buses and/or processors in the target MBS are specified. The optimality criteria are maximizing the speed and minimizing the number of interfaces