127 research outputs found
LUXOR: An FPGA Logic Cell Architecture for Efficient Compressor Tree Implementations
We propose two tiers of modifications to FPGA logic cell architecture to
deliver a variety of performance and utilization benefits with only minor area
overheads. In the irst tier, we augment existing commercial logic cell
datapaths with a 6-input XOR gate in order to improve the expressiveness of
each element, while maintaining backward compatibility. This new architecture
is vendor-agnostic, and we refer to it as LUXOR. We also consider a secondary
tier of vendor-speciic modifications to both Xilinx and Intel FPGAs, which we
refer to as X-LUXOR+ and I-LUXOR+ respectively. We demonstrate that compressor
tree synthesis using generalized parallel counters (GPCs) is further improved
with the proposed modifications. Using both the Intel adaptive logic module and
the Xilinx slice at the 65nm technology node for a comparative study, it is
shown that the silicon area overhead is less than 0.5% for LUXOR and 5-6% for
LUXOR+, while the delay increments are 1-6% and 3-9% respectively. We
demonstrate that LUXOR can deliver an average reduction of 13-19% in logic
utilization on micro-benchmarks from a variety of domains.BNN benchmarks
benefit the most with an average reduction of 37-47% in logic utilization,
which is due to the highly-efficient mapping of the XnorPopcount operation on
our proposed LUXOR+ logic cells.Comment: In Proceedings of the 2020 ACM/SIGDA International Symposium on
Field-Programmable Gate Arrays (FPGA'20), February 23-25, 2020, Seaside, CA,
US
Superfluid transport of information in turning flocks of starlings
Collective decision-making in biological systems requires all individuals in
the group to go through a behavioural change of state. During this transition,
the efficiency of information transport is a key factor to prevent cohesion
loss and preserve robustness. The precise mechanism by which natural groups
achieve such efficiency, though, is currently not fully understood. Here, we
present an experimental study of starling flocks performing collective turns in
the field. We find that the information to change direction propagates across
the flock linearly in time with negligible attenuation, hence keeping group
decoherence to a minimum. This result contrasts with current theories of
collective motion, which predict a slower and dissipative transport of
directional information. We propose a novel theory whose cornerstone is the
existence of a conserved spin current generated by the gauge symmetry of the
system. The theory turns out to be mathematically identical to that of
superfluid transport in liquid helium and it explains the dissipationless
propagating mode observed in turning flocks. Superfluidity also provides a
quantitative expression for the speed of propagation of the information,
according to which transport must be swifter the stronger the group's
orientational order. This prediction is verified by the data. We argue that the
link between strong order and efficient decision-making required by
superfluidity may be the adaptive drive for the high degree of behavioural
polarization observed in many living groups. The mathematical equivalence
between superfluid liquids and turning flocks is a compelling demonstration of
the far-reaching consequences of symmetry and conservation laws across
different natural systems
Price-Based Unit Commitment Electricity Storage Arbitrage with Piecewise Linear Price-Effects
Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies
<p>Abstract</p> <p>Background</p> <p>The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the "best" grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters.</p> <p>Results</p> <p>In this article, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric. The physical permutations of the rows and columns of the data matrix can be modeled as either a network flow problem or a traveling salesman problem. Cluster boundaries in one dimension are used to partition and re-order the other dimensions of the corresponding submatrices to generate biclusters. The performance of OREO is tested on (a) metabolite concentration data, (b) an image reconstruction matrix, (c) synthetic data with implanted biclusters, and gene expression data for (d) colon cancer data, (e) breast cancer data, as well as (f) yeast segregant data to validate the ability of the proposed method and compare it to existing biclustering and clustering methods.</p> <p>Conclusion</p> <p>We demonstrate that this rigorous global optimization method for biclustering produces clusters with more insightful groupings of similar entities, such as genes or metabolites sharing common functions, than other clustering and biclustering algorithms and can reconstruct underlying fundamental patterns in the data for several distinct sets of data matrices arising in important biological applications.</p
Combining optimisation and simulation in an energy systems analysis of a Swedish iron foundry
Integrierte Planung der mittelfristigen Komponenten- und Teilebedarfe für variantenreiche Serienprodukte
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