11,390 research outputs found

    Access to vectors in multi-module memories

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    The poor bandwidth obtained from memory when conflicts arise in the modules or in the interconnection network degrades the performance of computers. Address transformation schemes, such as interleaving, skewing and linear transformations, have been proposed to achieve conflict-free access for streams with constant stride. However, this is achieved only for some strides. In this paper, we summarize a mechanism to request the elements in an out-of-order way which allows to achieve conflict-free access for a larger number of strides. We study the cases of a single vector processor and of a vector multiprocessor system. For this latter case, we propose a synchronous mode of accessing memory that can be applied in SIMD machines or in MIMD systems with decoupled access and execution.Peer ReviewedPostprint (published version

    Conflict-free strides for vectors in matched memories

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    Address transformation schemes, such as skewing and linear transformations, have been proposed to achieve conflict-free access to one family of strides in vector processors with matched memories. The paper extends these schemes to achieve this conflict-free access for several families. The basic idea is to perform an out-of-order access to vectors of fixed length, equal to that of the vector registers of the processor. The hardware required is similar to that for the access in order.Peer ReviewedPostprint (author's final draft

    Memory Networks

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    We describe a new class of learning models called memory networks. Memory networks reason with inference components combined with a long-term memory component; they learn how to use these jointly. The long-term memory can be read and written to, with the goal of using it for prediction. We investigate these models in the context of question answering (QA) where the long-term memory effectively acts as a (dynamic) knowledge base, and the output is a textual response. We evaluate them on a large-scale QA task, and a smaller, but more complex, toy task generated from a simulated world. In the latter, we show the reasoning power of such models by chaining multiple supporting sentences to answer questions that require understanding the intension of verbs

    DFT and BIST of a multichip module for high-energy physics experiments

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    Engineers at Politecnico di Torino designed a multichip module for high-energy physics experiments conducted on the Large Hadron Collider. An array of these MCMs handles multichannel data acquisition and signal processing. Testing the MCM from board to die level required a combination of DFT strategie
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