Publications

High-Throughput Energy-Efficient LDPC Decoders Using Differential Binary Message Passing
Kevin Cushon
Saied Hemati
Camille Leroux
Shie Mannor
Warren J. Gross
In this paper, we present energy-efficient architectures for decoders of low-density parity check (LDPC) codes using the differential decodi… (see more)ng with binary message passing (DD-BMP) algorithm and its modified variant (MDD-BMP). We also propose an improved differential binary (IDB) decoding algorithm. These algorithms offer significant intrinsic advantages in the energy domain: simple computations, low interconnect complexity, and very high throughput, while achieving error correction performance up to within 0.25 dB of the offset min-sum algorithm. We report on fully parallel decoder implementations of (273, 191), (1023, 781), and (4095, 3367) finite geometry-based LDPC codes in 65 nm CMOS. Using the MDD-BMP algorithm, these decoders achieve respective areas of 0.28 mm2, 1.38 mm2, and 15.37 mm2, average throughputs of 37 Gbps, 75 Gbps, and 141 Gbps, and energy efficiencies of 4.9 pJ/bit, 13.2 pJ/bit, and 37.9 pJ/bit with a 1.0 V supply voltage in post-layout simulations. At a reduced supply voltage of 0.8 V, these decoders achieve respective throughputs of 26 Gbps, 54 Gbps, and 94 Gbps, and energy efficiencies of 3.1 pJ/bit, 8.2 pJ/bit, and 23.5 pJ/bit. We also report on a fully parallel implementation of IDB for the (2048, 1723) LDPC code specified in the IEEE 802.3an (10GBASE-T) standard. This decoder achieves an area of 1.44 mm2, average throughput of 172 Gbps, and an energy efficiency of 2.8 pJ/bit with a 1.0 V supply voltage; at 0.8 V, it achieves throughput of 116 Gbps and energy efficiency of 1.7 pJ/bit.
2014-01-31
IEEE Transactions on Signal Processing (published)
doi.org
Bayesian and grAphical Models for Biomedical Imaging
M. Jorge Cardoso
Ivor J. A. Simpson
Arbel, Tal
Doina Precup
Annemie Ribbens
2013-12-31
Lecture Notes in Computer Science (published)
doi.org
Machine Learning and Interpretation in Neuroimaging
Irina Rish
Georg Langs
Leila Wehbe
Guillermo Cecchi
Kai-min Kevin Chang
Brian G Murphy
2013-12-31
Lecture Notes in Computer Science (published)
doi.org
Experimental Algorithms
Samuel Rosat
Issmail ElHallaoui
François Soumis
Andrea Lodi
2013-10-31
Lecture Notes in Computer Science (published)
doi.org
Adaptive Multiset Stochastic Decoding of Non-Binary LDPC Codes
Alexandru Ciobanu
Saied Hemati
Warren J. Gross
We propose a non-binary stochastic decoding algorithm for low-density parity-check (LDPC) codes over GF(q) with degree two variable nodes, c… (see more)alled Adaptive Multiset Stochastic Algorithm (AMSA). The algorithm uses multisets, an extension of sets that allows multiple occurrences of an element, to represent probability mass functions that simplifies the structure of the variable nodes. The run-time complexity of one decoding cycle using AMSA is O(q) for conventional memory architectures, and O(1) if a custom memory architecture is used. Two fully-parallel AMSA decoders are implemented on FPGA for two (192,96) (2,4)-regular codes over GF(64) and GF(256), both achieving a maximum clock frequency of 108 MHz. The GF(64) decoder has a coded throughput of 65 Mb/s at Eb/N0=2.4 dB when using conventional memory, while a decoder using the custom memory version can achieve 698 Mb/s at the same Eb/N0. At a frame error rate (FER) of 2×10-6 the GF(64) version of the algorithm is only 0.04 dB away from the floating-point SPA performance, and for the GF(256) code the difference is 0.2 dB. To the best of our knowledge, this is the first fully parallel non-binary LDPC decoder over GF(256) reported in the literature.
2013-08-14
IEEE Transactions on Signal Processing (published)
doi.org
Multiscale Gossip for Efficient Decentralized Averaging in Wireless Packet Networks
Konstantinos I. Tsianos
Michael G. Rabbat
This paper describes and analyzes a hierarchical algorithm called Multiscale Gossip for solving the distributed average consensus problem in… (see more) wireless sensor networks. The algorithm proceeds by recursively partitioning a given network. Initially, nodes at the finest scale gossip to compute local averages. Then, using multi-hop communication and geographic routing to communicate between nodes that are not directly connected, these local averages are progressively fused up the hierarchy until the global average is computed. We show that the proposed hierarchical scheme with k=Θ(loglogn) levels of hierarchy is competitive with state-of-the-art randomized gossip algorithms in terms of message complexity, achieving ε-accuracy with high probability after O(n loglogn log[1/(ε)] ) single-hop messages. Key to our analysis is the way in which the network is recursively partitioned. We find that the above scaling law is achieved when subnetworks at scale j contain O(n(2/3)j) nodes; then the message complexity at any individual scale is O(n log[1/ε]). Another important consequence of the hierarchical construction is that the longest distance over which messages are exchanged is O(n1/3) hops (at the highest scale), and most messages (at lower scales) travel shorter distances. In networks that use link-level acknowledgements, this results in less congestion and resource usage by reducing message retransmissions. Simulations illustrate that the proposed scheme is more efficient than state-of-the-art randomized gossip algorithms based on averaging along paths.
2013-04-30
IEEE Transactions on Signal Processing (published)
doi.org
arxiv.org
Active learning of multiple source multiple destination topologies
Pegah Sattari
Maciej Kurant
Animashree Anandkumar
Athina Markopoulou
Michael G. Rabbat
We consider the problem of inferring the topology of an M-by-N network by sending probes between M sources and N receivers. Prior work has s… (see more)hown that this problem can be decomposed into two parts: first, infer smaller subnetwork components (i.e., 1-by-N's or 2-by-2's) and then merge these components to identify the M-by-N topology. In this paper, we focus on the second part. In particular, we assume that a 1by-N topology is given and that all 2-by-2 components can be queried and learned using end-to-end probes. The problem is which 2-by-2's to query and how to merge them with the 1-byN, so as to exactly identify the 2-by-N topology, and optimize a number of performance metrics including measurement traffic, time complexity, and memory usage. We provide a lower bound, ⌈N/2⌉, on the number of 2-by-2's required by any active learning algorithm and we also propose a greedy algorithm that is nearoptimal and efficient in practice. It follows a bottom-up approach: at every step, it selects two receivers, queries the corresponding 2-by-2, and merges it with the given 1-by-N. The algorithm requires exactly N - 1 steps, which is much less than all (N:2) possible 2-by-2's, and it correctly identifies the 2-by-N topology.
2013-03-19
Annual Conference on Information Sciences and Systems (published)
doi.org
Large‐scale geographic patterns of diversity and community structure of pelagic crustacean zooplankton in <scp>C</scp>anadian lakes
Bernadette Pinel‐Alloul
Adrien André
Pierre Legendre
Jeffrey A. Cardille
Kasimierz Patalas
Alex Salki
The richness‐energy theory explains diversity patterns of freshwater crustacean zooplankton in Canadian ecoprovinces. Solar radiation is t… (see more)he best predictor explaining regional species richness in ecoprovinces and community structure of pelagic crustaceans in Canadian lakes.
2013-01-28
Global Ecology and Biogeography (published)
doi.org
A Semi-Parallel Successive-Cancellation Decoder for Polar Codes
Camille Leroux
Alexandre J. Raymond
Gabi Sarkis
Warren J. Gross
Polar codes are a recently discovered family of capacity-achieving codes that are seen as a major breakthrough in coding theory. Motivated b… (see more)y the recent rapid progress in the theory of polar codes, we propose a semi-parallel architecture for the implementation of successive cancellation decoding. We take advantage of the recursive structure of polar codes to make efficient use of processing resources. The derived architecture has a very low processing complexity while the memory complexity remains similar to that of previous architectures. This drastic reduction in processing complexity allows very large polar code decoders to be implemented in hardware. An N=217 polar code successive cancellation decoder is implemented in an FPGA. We also report synthesis results for ASIC.
2013-01-14
IEEE Transactions on Signal Processing (published)
doi.org
Computation, Logic, Games, and Quantum Foundations. The Many Facets of Samson Abramsky
Bob. Coecke
Luke Ong
Prakash Panangaden
Samson. Abramsky
2012-12-31
Computation, Logic, Games, and Quantum Foundations (published)
doi.org
Statistical Language and Speech Processing
Yoshua Bengio
Fethi Bougares
Horia Cucu
Corneliu Burileanu
Myung-Jae Kim
Il-ho Yang
Jordan Rodu
Dean Phillips Foster
Weichen Wu
Stefan Bott
Felix Stahlberg
Luis A. Trindade
Hao Wang
2012-12-31
Lecture Notes in Computer Science (published)
doi.org
Algorithms in Bioinformatics
P. Agarwal
Tatsuya Akutsu
Amir Amihood
Alberto Apostolico
C. Benham
Gary Gustaf Benson
Mathieu Blanchette
Nadia El-Mabrouk
Olivier Gascuel
Raffaele Giancarlo
R. Guigó
Michael Hallet
D. Huson
G. Kucherov
Michelle R. Lacey
Jens Lagergren
Giuseppe Lancia
Gad M. Landau
Thierry. Lecroq
B. Moret … (see 21 more)
S. Morishita
Elchanan Mossel
Vincent Moulton
Lior S. Pachter
Knut Reinert
I. Rigoutsos
David Sankoff
Sophie Schbath
Eran Segal
Charles Semple
J. Setubal
Roded Sharan
S. Skiena
Jens Stoye
Esko Ukkonen
Lisa Allen Vawter
Alfonso Valencia
Tandy J. Warnow
Lusheng Wang
Rita Casadio
Gene Myers
2012-09-30
Lecture Notes in Computer Science (published)
doi.org