6,762 research outputs found
Locally-Stable Macromodels of Integrated Digital Devices for Multimedia Applications
This paper addresses the development of accurate and efficient behavioral models of digital integrated circuits for the assessment of high-speed systems. Device models are based on suitable parametric expressions estimated from port transient responses and are effective at system level, where the quality of functional signals and the impact of supply noise need to be simulated. A potential limitation of some state-of-the-art modeling techniques resides in hidden instabilities manifesting themselves in the use of models, without being evident in the building phase of the same models. This contribution compares three recently-proposed model structures, and selects the local-linear state-space modeling technique as an optimal candidate for the signal integrity assessment of data links. In fact, this technique combines a simple verification of the local stability of models with a limited model size and an easy implementation in commercial simulation tools. An application of the proposed methodology to a real problem involving commercial devices and a data-link of a wireless device demonstrates the validity of this approac
Nonlinear Black-Box Models of Digital Integrated Circuits via System Identification
This Thesis concerns the development of numerical macromodels of digi-
tal Integrated Circuits input/output buffers. Such models are of paramount
importance for the system-level simulation required for the assessment of Sig-
nal Integrity and Electromagnetic Compatibility effects in high-performance
electronic equipments via system-level simulations.
In order to obtain accurate and efficient macromodels, we concentrate on
the black-box modeling approach, exploiting system identification methods.
The present study contributes to the systematic discussion of the IC mod-
eling process, in order to obtain macromodels that can overcome strengths
and limitations of the methodologies presented so far. The performances of
different parametric representations, as Sigmoidal Basis Functions (SBF) ex-
pansions, Echo State Networks (ESN) and Local Linear State-Space (LLSS)
models are investigated. All representations have proven capabilities for the
modeling of unknown nonlinear dynamic systems and are good candidates too
be used for the modeling problem at hand. For each model representation,
the most suitable estimation algorithm is considered and a systematic analy-
sis is performed to highlight advantages and limitations. For this analysis,
the modeling process is applied to a synthetic nonlinear device representative
of IC ports, and designed to generate stiff responses.
The tests carried out show that LLSS models provide the best overall
performance for the modeling of digital devices, even with strong nonlinear
dynamics. LLSS models can be estimated by means of an efficient algorithm
providing a unique solution. Local stability of models is preconditioned and
verified a posteriori.
The effectiveness of the modeling process based on LLSS representations
is verified by applying the proposed technique to the modeling of real devices
involved in a realistic data communication link (an RF-to-Digital interface
used in mobile phones). The obtained macromodels have been successfully
used to predict both the functional signals and the power supply and ground
fluctuations. Besides, they turn out to be very efficient, providing a signifi-
cant simulation speed-up for the complete data link
Opening the “Black Box” of Silicon Chip Design in Neuromorphic Computing
Neuromorphic computing, a bio-inspired computing architecture that transfers neuroscience to silicon chip, has potential to achieve the same level of computation and energy efficiency as mammalian brains. Meanwhile, three-dimensional (3D) integrated circuit (IC) design with non-volatile memory crossbar array uniquely unveils its intrinsic vector-matrix computation with parallel computing capability in neuromorphic computing designs. In this chapter, the state-of-the-art research trend on electronic circuit designs of neuromorphic computing will be introduced. Furthermore, a practical bio-inspired spiking neural network with delay-feedback topology will be discussed. In the endeavor to imitate how human beings process information, our fabricated spiking neural network chip has capability to process analog signal directly, resulting in high energy efficiency with small hardware implementation cost. Mimicking the neurological structure of mammalian brains, the potential of 3D-IC implementation technique with memristive synapses is investigated. Finally, applications on the chaotic time series prediction and the video frame recognition will be demonstrated
The SLH framework for modeling quantum input-output networks
Many emerging quantum technologies demand precise engineering and control
over networks consisting of quantum mechanical degrees of freedom connected by
propagating electromagnetic fields, or quantum input-output networks. Here we
review recent progress in theory and experiment related to such quantum
input-output networks, with a focus on the SLH framework, a powerful modeling
framework for networked quantum systems that is naturally endowed with
properties such as modularity and hierarchy. We begin by explaining the
physical approximations required to represent any individual node of a network,
eg. atoms in cavity or a mechanical oscillator, and its coupling to quantum
fields by an operator triple . Then we explain how these nodes can be
composed into a network with arbitrary connectivity, including coherent
feedback channels, using algebraic rules, and how to derive the dynamics of
network components and output fields. The second part of the review discusses
several extensions to the basic SLH framework that expand its modeling
capabilities, and the prospects for modeling integrated implementations of
quantum input-output networks. In addition to summarizing major results and
recent literature, we discuss the potential applications and limitations of the
SLH framework and quantum input-output networks, with the intention of
providing context to a reader unfamiliar with the field.Comment: 60 pages, 14 figures. We are still interested in receiving
correction
Efficient Synthesis of Room Acoustics via Scattering Delay Networks
An acoustic reverberator consisting of a network of delay lines connected via
scattering junctions is proposed. All parameters of the reverberator are
derived from physical properties of the enclosure it simulates. It allows for
simulation of unequal and frequency-dependent wall absorption, as well as
directional sources and microphones. The reverberator renders the first-order
reflections exactly, while making progressively coarser approximations of
higher-order reflections. The rate of energy decay is close to that obtained
with the image method (IM) and consistent with the predictions of Sabine and
Eyring equations. The time evolution of the normalized echo density, which was
previously shown to be correlated with the perceived texture of reverberation,
is also close to that of IM. However, its computational complexity is one to
two orders of magnitude lower, comparable to the computational complexity of a
feedback delay network (FDN), and its memory requirements are negligible
Self-similar correlation function in brain resting-state fMRI
Adaptive behavior, cognition and emotion are the result of a bewildering
variety of brain spatiotemporal activity patterns. An important problem in
neuroscience is to understand the mechanism by which the human brain's 100
billion neurons and 100 trillion synapses manage to produce this large
repertoire of cortical configurations in a flexible manner. In addition, it is
recognized that temporal correlations across such configurations cannot be
arbitrary, but they need to meet two conflicting demands: while diverse
cortical areas should remain functionally segregated from each other, they must
still perform as a collective, i.e., they are functionally integrated. Here, we
investigate these large-scale dynamical properties by inspecting the character
of the spatiotemporal correlations of brain resting-state activity. In physical
systems, these correlations in space and time are captured by measuring the
correlation coefficient between a signal recorded at two different points in
space at two different times. We show that this two-point correlation function
extracted from resting-state fMRI data exhibits self-similarity in space and
time. In space, self-similarity is revealed by considering three successive
spatial coarse-graining steps while in time it is revealed by the 1/f frequency
behavior of the power spectrum. The uncovered dynamical self-similarity implies
that the brain is spontaneously at a continuously changing (in space and time)
intermediate state between two extremes, one of excessive cortical integration
and the other of complete segregation. This dynamical property may be seen as
an important marker of brain well-being both in health and disease.Comment: 14 pages 13 figures; published online before print September 2
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