196,515 research outputs found
Using Multi-Threshold Threshold Gates in RTD-based Logic Design. A Case Study
The basic building blocks for Resonant Tunnelling Diode (RTD) logic circuits
are Threshold Gates (TGs) instead of the conventional Boolean gates (AND, OR,
NAND, NOR) due to the fact that, when designing with RTDs, threshold gates can
be implemented as efficiently as conventional ones, but realize more complex
functions. Recently, RTD structures implementing Multi-Threshold Threshold
Gates (MTTGs) have been proposed which further increase the functionality of
the original TGs while maintaining their operating principle and allowing also
the implementation of nanopipelining at the gate level. This paper describes
the design of n-bit adders using these MTTGs. A comparison with a design based
on TGs is carried out showing advantages in terms of latency, device counts and
power consumption.Comment: Submitted on behalf of TIMA Editions
(http://irevues.inist.fr/tima-editions
An Enhanced Multiway Sorting Network Based on n-Sorters
Merging-based sorting networks are an important family of sorting networks.
Most merge sorting networks are based on 2-way or multi-way merging algorithms
using 2-sorters as basic building blocks. An alternative is to use n-sorters,
instead of 2-sorters, as the basic building blocks so as to greatly reduce the
number of sorters as well as the latency. Based on a modified Leighton's
columnsort algorithm, an n-way merging algorithm, referred to as SS-Mk, that
uses n-sorters as basic building blocks was proposed. In this work, we first
propose a new multiway merging algorithm with n-sorters as basic building
blocks that merges n sorted lists of m values each in 1 + ceil(m/2) stages (n
<= m). Based on our merging algorithm, we also propose a sorting algorithm,
which requires O(N log2 N) basic sorters to sort N inputs. While the asymptotic
complexity (in terms of the required number of sorters) of our sorting
algorithm is the same as the SS-Mk, for wide ranges of N, our algorithm
requires fewer sorters than the SS-Mk. Finally, we consider a binary sorting
network, where the basic sorter is implemented in threshold logic and scales
linearly with the number of inputs, and compare the complexity in terms of the
required number of gates. For wide ranges of N, our algorithm requires fewer
gates than the SS-Mk.Comment: 13 pages, 14 figure
When Does a Mixture of Products Contain a Product of Mixtures?
We derive relations between theoretical properties of restricted Boltzmann
machines (RBMs), popular machine learning models which form the building blocks
of deep learning models, and several natural notions from discrete mathematics
and convex geometry. We give implications and equivalences relating
RBM-representable probability distributions, perfectly reconstructible inputs,
Hamming modes, zonotopes and zonosets, point configurations in hyperplane
arrangements, linear threshold codes, and multi-covering numbers of hypercubes.
As a motivating application, we prove results on the relative representational
power of mixtures of product distributions and products of mixtures of pairs of
product distributions (RBMs) that formally justify widely held intuitions about
distributed representations. In particular, we show that a mixture of products
requiring an exponentially larger number of parameters is needed to represent
the probability distributions which can be obtained as products of mixtures.Comment: 32 pages, 6 figures, 2 table
A Silicon Surface Code Architecture Resilient Against Leakage Errors
Spin qubits in silicon quantum dots are one of the most promising building
blocks for large scale quantum computers thanks to their high qubit density and
compatibility with the existing semiconductor technologies. High fidelity
single-qubit gates exceeding the threshold of error correction codes like the
surface code have been demonstrated, while two-qubit gates have reached 98\%
fidelity and are improving rapidly. However, there are other types of error ---
such as charge leakage and propagation --- that may occur in quantum dot arrays
and which cannot be corrected by quantum error correction codes, making them
potentially damaging even when their probability is small. We propose a surface
code architecture for silicon quantum dot spin qubits that is robust against
leakage errors by incorporating multi-electron mediator dots. Charge leakage in
the qubit dots is transferred to the mediator dots via charge relaxation
processes and then removed using charge reservoirs attached to the mediators. A
stabiliser-check cycle, optimised for our hardware, then removes the
correlations between the residual physical errors. Through simulations we
obtain the surface code threshold for the charge leakage errors and show that
in our architecture the damage due to charge leakage errors is reduced to a
similar level to that of the usual depolarising gate noise. Spin leakage errors
in our architecture are constrained to only ancilla qubits and can be removed
during quantum error correction via reinitialisations of ancillae, which ensure
the robustness of our architecture against spin leakage as well. Our use of an
elongated mediator dots creates spaces throughout the quantum dot array for
charge reservoirs, measuring devices and control gates, providing the
scalability in the design
spChains: A Declarative Framework for Data Stream Processing in Pervasive Applications
Pervasive applications rely on increasingly complex streams of sensor data continuously captured from the physical world. Such data is crucial to enable applications to ``understand'' the current context and to infer the right actions to perform, be they fully automatic or involving some user decisions. However, the continuous nature of such streams, the relatively high throughput at which data is generated and the number of sensors usually deployed in the environment, make direct data handling practically unfeasible. Data not only needs to be cleaned, but it must also be filtered and aggregated to relieve higher level algorithms from near real-time handling of such massive data flows. We propose here a stream-processing framework (spChains), based upon state-of-the-art stream processing engines, which enables declarative and modular composition of stream processing chains built atop of a set of extensible stream processing blocks. While stream processing blocks are delivered as a standard, yet extensible, library of application-independent processing elements, chains can be defined by the pervasive application engineering team. We demonstrate the flexibility and effectiveness of the spChains framework on two real-world applications in the energy management and in the industrial plant management domains, by evaluating them on a prototype implementation based on the Esper stream processo
Multi-Octave Frequency Comb from an Ultra-Low-Threshold Nanophotonic Parametric Oscillator
Ultrabroadband frequency combs coherently unite distant portions of the
electromagnetic spectrum. They underpin discoveries in ultrafast science and
serve as the building blocks of modern photonic technologies. Despite
tremendous progress in integrated sources of frequency combs, achieving
multi-octave operation on chip has remained elusive mainly because of the
energy demand of typical spectral broadening processes. Here we break this
barrier and demonstrate multi-octave frequency comb generation using an optical
parametric oscillator (OPO) in nanophotonic lithium niobate with only
femtojoules of pump energy. The energy-efficient and robust coherent spectral
broadening occurs far above the oscillation threshold of the OPO and detuned
from its linear synchrony with the pump. We show that the OPO can undergo a
temporal self-cleaning mechanism by transitioning from an incoherent operation
regime, which is typical for operation far above threshold, to an ultrabroad
coherent regime, corresponding to the nonlinear phase compensating the OPO
cavity detuning. Such a temporal self-cleaning mechanism and the subsequent
multi-octave coherent spectrum has not been explored in previous OPO designs
and features a relaxed requirement for the quality factor and relatively narrow
spectral coverage of the cavity. We achieve orders of magnitude reduction in
the energy requirement compared to the other techniques, confirm the coherence
of the comb, and present a path towards more efficient and wider spectral
broadening. Our results pave the way for ultrashort-pulse and ultrabroadband
on-chip nonlinear photonic systems for numerous applications.Comment: 8 pages, 4 figure
Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
Multitask Learning is a learning paradigm that deals with multiple different
tasks in parallel and transfers knowledge among them. XOF, a Learning
Classifier System using tree-based programs to encode building blocks
(meta-features), constructs and collects features with rich discriminative
information for classification tasks in an observed list. This paper seeks to
facilitate the automation of feature transferring in between tasks by utilising
the observed list. We hypothesise that the best discriminative features of a
classification task carry its characteristics. Therefore, the relatedness
between any two tasks can be estimated by comparing their most appropriate
patterns. We propose a multiple-XOF system, called mXOF, that can dynamically
adapt feature transfer among XOFs. This system utilises the observed list to
estimate the task relatedness. This method enables the automation of
transferring features. In terms of knowledge discovery, the resemblance
estimation provides insightful relations among multiple data. We experimented
mXOF on various scenarios, e.g. representative Hierarchical Boolean problems,
classification of distinct classes in the UCI Zoo dataset, and unrelated tasks,
to validate its abilities of automatic knowledge-transfer and estimating task
relatedness. Results show that mXOF can estimate the relatedness reasonably
between multiple tasks to aid the learning performance with the dynamic feature
transferring.Comment: accepted by The Genetic and Evolutionary Computation Conference
(GECCO 2020
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