10,458 research outputs found
High accuracy computation with linear analog optical systems: a critical study
High accuracy optical processors based on the algorithm of digital multiplication by analog convolution (DMAC) are studied for ultimate performance limitations. Variations of optical processors that perform high accuracy vector-vector inner products are studied in abstract and with specific examples. It is concluded that the use of linear analog optical processors in performing digital computations with DMAC leads to impractical requirements for the accuracy of analog optical systems and the complexity of postprocessing electronics
The Scalability-Efficiency/Maintainability-Portability Trade-off in Simulation Software Engineering: Examples and a Preliminary Systematic Literature Review
Large-scale simulations play a central role in science and the industry.
Several challenges occur when building simulation software, because simulations
require complex software developed in a dynamic construction process. That is
why simulation software engineering (SSE) is emerging lately as a research
focus. The dichotomous trade-off between scalability and efficiency (SE) on the
one hand and maintainability and portability (MP) on the other hand is one of
the core challenges. We report on the SE/MP trade-off in the context of an
ongoing systematic literature review (SLR). After characterizing the issue of
the SE/MP trade-off using two examples from our own research, we (1) review the
33 identified articles that assess the trade-off, (2) summarize the proposed
solutions for the trade-off, and (3) discuss the findings for SSE and future
work. Overall, we see evidence for the SE/MP trade-off and first solution
approaches. However, a strong empirical foundation has yet to be established;
general quantitative metrics and methods supporting software developers in
addressing the trade-off have to be developed. We foresee considerable future
work in SSE across scientific communities.Comment: 9 pages, 2 figures. Accepted for presentation at the Fourth
International Workshop on Software Engineering for High Performance Computing
in Computational Science and Engineering (SEHPCCSE 2016
Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations
Large-scale (or massive) multiple-input multiple-output (MIMO) is expected to
be one of the key technologies in next-generation multi-user cellular systems,
based on the upcoming 3GPP LTE Release 12 standard, for example. In this work,
we propose - to the best of our knowledge - the first VLSI design enabling
high-throughput data detection in single-carrier frequency-division multiple
access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate
matrix inversion algorithm relying on a Neumann series expansion, which
substantially reduces the complexity of linear data detection. We analyze the
associated error, and we compare its performance and complexity to those of an
exact linear detector. We present corresponding VLSI architectures, which
perform exact and approximate soft-output detection for large-scale MIMO
systems with various antenna/user configurations. Reference implementation
results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to
achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale
MIMO system. We finally provide a performance/complexity trade-off comparison
using the presented FPGA designs, which reveals that the detector circuit of
choice is determined by the ratio between BS antennas and users, as well as the
desired error-rate performance.Comment: To appear in the IEEE Journal of Selected Topics in Signal Processin
Flexible Multi-layer Sparse Approximations of Matrices and Applications
The computational cost of many signal processing and machine learning
techniques is often dominated by the cost of applying certain linear operators
to high-dimensional vectors. This paper introduces an algorithm aimed at
reducing the complexity of applying linear operators in high dimension by
approximately factorizing the corresponding matrix into few sparse factors. The
approach relies on recent advances in non-convex optimization. It is first
explained and analyzed in details and then demonstrated experimentally on
various problems including dictionary learning for image denoising, and the
approximation of large matrices arising in inverse problems
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