Vision algorithms for hypercube machines

Several commercial hypercube parallel processors with the potential to deliver massive parallelism cost-effectively have been announced recently. They open the door to a wide variety of application areas that could benefit from parallelism. Computer vision is one of these application areas. This paper develops a general model for hypercube machines, and uses it to show how vision algorithms can be executed on hypercubes. In particular, the steps in the problem of thick-film inspection are used as a concrete example. The time needed to complete a typical inspection is used to demonstrate the performance of hypercube machines. Experimental results from a hypercube machine illustrate the potential use of such machines.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/26820/1/0000379.pd

Performance analysis of pyramid mapping algorithms for the hypercube

Comparative performance analysis of algorithms that map pyramids and multilevel structures onto the hypercube are presented. The pyramid structure is appropriate for low-level and intermediate-level computer vision algorithms. It is not only efficient for the support of both local and global operations but also capable of supporting the implementation of multilevel solvers. Nevertheless, pyramids lack the capability of efficient implementation of the majority of scientific algorithms and their cost may become unacceptably high. On a different horizon, hypercube machines have widely been used in the field of parallel computing due to their small diameter, high degree of fault tolerance, and rich interconnection that permits fast communication at a reasonable cost. As a result, hypercube machines can efficiently emulate pyramids. Therefore, the characteristics which make hypercube machines useful scientific processors also make them efficient image processors. Two algorithms which have been developed for the efficient mapping of the pyramid onto the hypercube are discussed in this thesis. The algorithm proposed by Stout [4] requires a hypercube with a number of processing elements (PEs) which is equal to the number of nodes in the base of the pyramid. This algorithm can activate only one level of the pyramid at a time. In contrast, the algorithm proposed by Patel and Ziavras [7] requires the same number of PEs as Stout\u27s algorithm but allows the concurren simulation of multiple levels, as long as the base level is not involved in the set of pyramid levels that need to be simulated at the same time. This low-cost algorithm yields higher performance through high utilization of PEs. However it performs slightly worse than Stout\u27s algorithm when only one level is active at a time. Patel and Ziavras\u27 algorithm performs much better than Stout\u27s algorithm when all levels, excluding the leaf level, are active concurrently. The comparative analysis of these two algorithms is based on the incorporation of simulation results for some image processing algorithms which are perimeter counting, image convolution, and segmentation

White paper: A plan for cooperation between NASA and DARPA to establish a center for advanced architectures

Large, complex computer systems require many years of development. It is recognized that large scale systems are unlikely to be delivered in useful condition unless users are intimately involved throughout the design process. A mechanism is described that will involve users in the design of advanced computing systems and will accelerate the insertion of new systems into scientific research. This mechanism is embodied in a facility called the Center for Advanced Architectures (CAA). CAA would be a division of RIACS (Research Institute for Advanced Computer Science) and would receive its technical direction from a Scientific Advisory Board established by RIACS. The CAA described here is a possible implementation of a center envisaged in a proposed cooperation between NASA and DARPA

Parallel Architectures for Planetary Exploration Requirements (PAPER)

The Parallel Architectures for Planetary Exploration Requirements (PAPER) project is essentially research oriented towards technology insertion issues for NASA's unmanned planetary probes. It was initiated to complement and augment the long-term efforts for space exploration with particular reference to NASA/LaRC's (NASA Langley Research Center) research needs for planetary exploration missions of the mid and late 1990s. The requirements for space missions as given in the somewhat dated Advanced Information Processing Systems (AIPS) requirements document are contrasted with the new requirements from JPL/Caltech involving sensor data capture and scene analysis. It is shown that more stringent requirements have arisen as a result of technological advancements. Two possible architectures, the AIPS Proof of Concept (POC) configuration and the MAX Fault-tolerant dataflow multiprocessor, were evaluated. The main observation was that the AIPS design is biased towards fault tolerance and may not be an ideal architecture for planetary and deep space probes due to high cost and complexity. The MAX concepts appears to be a promising candidate, except that more detailed information is required. The feasibility for adding neural computation capability to this architecture needs to be studied. Key impact issues for architectural design of computing systems meant for planetary missions were also identified

Hypercube technology

The JPL designed MARKIII hypercube supercomputer has been in application service since June 1988 and has had successful application to a broad problem set including electromagnetic scattering, discrete event simulation, plasma transport, matrix algorithms, neural network simulation, image processing, and graphics. Currently, problems that are not homogeneous are being attempted, and, through this involvement with real world applications, the software is evolving to handle the heterogeneous class problems efficiently

A Genetic Programming Approach to Designing Convolutional Neural Network Architectures

The convolutional neural network (CNN), which is one of the deep learning models, has seen much success in a variety of computer vision tasks. However, designing CNN architectures still requires expert knowledge and a lot of trial and error. In this paper, we attempt to automatically construct CNN architectures for an image classification task based on Cartesian genetic programming (CGP). In our method, we adopt highly functional modules, such as convolutional blocks and tensor concatenation, as the node functions in CGP. The CNN structure and connectivity represented by the CGP encoding method are optimized to maximize the validation accuracy. To evaluate the proposed method, we constructed a CNN architecture for the image classification task with the CIFAR-10 dataset. The experimental result shows that the proposed method can be used to automatically find the competitive CNN architecture compared with state-of-the-art models.Comment: This is the revised version of the GECCO 2017 paper. The code of our method is available at https://github.com/sg-nm/cgp-cn