Publications
Scalable prototyping testbed for MMW imager system
Summary
Summary
A prototyping testbed for an experimental millimeter-wave multiple-imput multiple-output (MIMO) radar system for security applications in high foot-traffic areas will be presented. The system is designed for flexible operation at a 10 Hz video rate, enabled by high-speed electronic scanning and real-time signal processing. Overall imaging system costs are reduced...
In-storage embedded accelerator for sparse pattern processing
Summary
Summary
We present a novel architecture for sparse pattern processing, using flash storage with embedded accelerators. Sparse pattern processing on large data sets is the essence of applications such as document search, natural language processing, bioinformatics, subgraph matching, machine learning, and graph processing. One slice of our prototype accelerator is capable...
Novel graph processor architecture, prototype system, and results
Summary
Summary
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 graph...
Very large graphs for information extraction (VLG) - summary of first-year proof-of-concept study
Summary
Summary
In numerous application domains relevant to the Department of Defense and the Intelligence Community, data of interest take the form of entities and the relationships between them, and these data are commonly represented as graphs. Under the Very Large Graphs for Information Extraction effort--a one-year proof-of-concept study--MIT LL developed novel...
Sparse volterra systems: theory and practice
Summary
Summary
Nonlinear effects limit analog circuit performance, causing both in-band and out-of-band distortion. The classical Volterra series provides an accurate model of many nonlinear systems, but the number of parameters grows extremely quickly as the memory depth and polynomial order are increased. Recently, concepts from compressed sensing have been applied to...
Novel graph processor architecture
Summary
Summary
Graph algorithms are increasingly used in applications that exploit large databases. However, conventional processor architectures are hard-pressed to handle the throughput and memory requirements of graph computation. Lincoln Laboratory's graph-processor architecture represents a fundamental rethinking of architectures. It utilizes innovations that include high-bandwidth three-dimensional (3D) communication links, a sparse matrix-based...
Driving big data with big compute
Summary
Summary
Big Data (as embodied by Hadoop clusters) and Big Compute (as embodied by MPI clusters) provide unique capabilities for storing and processing large volumes of data. Hadoop clusters make distributed computing readily accessible to the Java community and MPI clusters provide high parallel efficiency for compute intensive workloads. Bringing the...
Scalable cryptographic authentication for high performance computing
Summary
Summary
High performance computing (HPC) uses supercomputers and computing clusters to solve large computational problems. Frequently HPC resources are shared systems and access to restricted data sets or resources must be authenticated. These authentication needs can take multiple forms, both internal and external to the HPC cluster. A computational stack that...
Driving big data with big compute
Summary
Summary
Big Data (as embodied by Hadoop clusters) and Big Compute (as embodied by MPI clusters) provide unique capabilities for storing and processing large volumes of data. Hadoop clusters make distributed computing readily accessible to the Java community and MPI clusters provide high parallel efficiency for compute intensive workloads. Bringing the...
A scalable signal processing architecture for massive graph analysis
Summary
Summary
In many applications, it is convenient to represent data as a graph, and often these datasets will be quite large. This paper presents an architecture for analyzing massive graphs, with a focus on signal processing applications such as modeling, filtering, and signal detection. We describe the architecture, which covers the...