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The need to analyze increasingly larger graph datasets has driven the exploration of new methods and unique system architectures for graph processing. One such method moves away from the typical edge- and vertex-centric approaches and describes graph algorithms using linear-algebra operations, bringing the added benefits of predictable data-access patterns and ease of implementation. The performance...
Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are inadequate for handling the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a rethinking of parallel architectures for graph problems. Our processor utilizes innovations that include a sparse matrix-based...
As the computational performance of microprocessors continues to grow through the integration of an increasing number of processing cores on a single die, the interconnection network has become the central subsystem for providing the communications infrastructure among the on-chip cores as well as to off-chip memory. Silicon nanophotonics as an interconnect technology offers several promising benefits...
As advancements in CMOS technology trend toward ever increasing core counts in chip multiprocessors for high-performance embedded computing, the discrepancy between on- and off-chip communication bandwidth continues to widen due to the power and spatial constraints of electronic off-chip signaling. Silicon photonics-based communication offers many advantages over electronics for network-on-chip design,...
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