Robust, High-Speed Network Design for Large-Scale Multiprocessing

As multiprocessor system size scales upward, two important aspects of multiprocessor systems will generally get worse rather than better: (1) interprocessor communication latency will increase and (2) the probability that some component in the system will fail will increase. These problems can prevent us from realizing the potential benefits of large-scale multiprocessing. In this report we consider the problem of designing networks which simultaneously minimize communication latency while maximizing fault tolerance. Using a synergy of techniques including connection topologies, routing protocols, signalling techniques, and packaging technologies we assemble integrated, system-level solutions to this network design problem.

A Detailed Look at Scale and Translation Invariance in a Hierarchical Neural Model of Visual Object Recognition

The HMAX model has recently been proposed by Riesenhuber & Poggio as a hierarchical model of position- and size-invariant object recognition in visual cortex. It has also turned out to model successfully a number of other properties of the ventral visual stream (the visual pathway thought to be crucial for object recognition in cortex), and particularly of (view-tuned) neurons in macaque inferotemporal cortex, the brain area at the top of the ventral stream. The original modeling study only used ``paperclip'' stimuli, as in the corresponding physiology experiment, and did not explore systematically how model units' invariance properties depended on model parameters. In this study, we aimed at a deeper understanding of the inner workings of HMAX and its performance for various parameter settings and ``natural'' stimulus classes. We examined HMAX responses for different stimulus sizes and positions systematically and found a dependence of model units' responses on stimulus position for which a quantitative description is offered. Interestingly, we find that scale invariance properties of hierarchical neural models are not independent of stimulus class, as opposed to translation invariance, even though both are affine transformations within the image plane.