high_scalability high_scalability-2010 high_scalability-2010-826 knowledge-graph by maker-knowledge-mining
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Introduction: My apologies if you were looking for a post about cell phones. This post is about high density nanodevices. It's a follow up to How will memristors change everything? for those wishing to pursue these revolutionary ideas in more depth. This is one of those areas where if you are in the space then there's a lot of available information and if you are on the outside then it doesn't even seem to exist. Fortunately, Ben Chandler from The SyNAPSE Project , was kind enough to point me to a great set of presentations given at the 12th IEEE CNNA - International Workshop on Cellular Nanoscale Networks and their Applications - Towards Megaprocessor Computing. WARNING: these papers contain extreme technical content. If you are like me and you aren't an electrical engineer, much of it may make a sort of surface sense, but the deep and twisty details will fly over head. For the more software minded there are a couple more accessible presentations: Intelligent Machines built with Memristiv
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1 My apologies if you were looking for a post about cell phones. [sent-1, score-0.056]
2 Fortunately, Ben Chandler from The SyNAPSE Project , was kind enough to point me to a great set of presentations given at the 12th IEEE CNNA - International Workshop on Cellular Nanoscale Networks and their Applications - Towards Megaprocessor Computing. [sent-6, score-0.104]
3 Here a few excerpts from the presentations, just things I found particularly interesting. [sent-12, score-0.056]
4 From Greg Snider's talk we see the programming model in a little more detail than was exposed before: I get the vibe that the key notion is applying functions to arrays in parallel. [sent-16, score-0.069]
5 This seems to have more of the flavor of a discrete even simulation with a clock stepping through state machines that operate on cells in parallel. [sent-19, score-0.157]
6 Wire delay is bigger than gate delay, hence communication speed is limited (synchrony radius) Hence: The precedence of Locality ~ i. [sent-25, score-0.416]
7 ), (ii) to provide the framework for designing the algorithms with elementary array instructions, and (iii) to serve as the starting phase for the Virtual to Physical Cellular Machine mapping. [sent-28, score-0.077]
8 scalar, vector, or matrix signals), (iv) global memories of different data types M, organized in qualitatively different sizes and access times (e. [sent-31, score-0.113]
9 Tasks, the algorithms to be implemented, are defined on the Data/Memory representations of the Virtual Cellular Machine Various data representations for Topographic (e. [sent-34, score-0.271]
10 Markov processes, algebra, number theory) problems New Principles the role of the geometric address of a single processor in an array is introducing new algorithmic principles: the precedence of geometric locality (due to the physical and logical locality), i. [sent-38, score-0.816]
11 A side constraint is the number of communication pins (contacts). [sent-42, score-0.065]
12 New principles of Computational Complexity The algorithmic and physical complexity measures of these many core architectures will be eventually different compared to the single processor systems – abandoning the asymptotic framework (what is big? [sent-43, score-0.523]
13 With Ω cores and M total memory the finite virtual algorithmic complexity Ca = Ca(Ω , M ) The finite physical computational complexity of an algorithm is measured by a proper mix of speed, power, area, and accuracy. [sent-46, score-0.782]
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