2,528 research outputs found
Quantum Mechanics of a Rotating Billiard
Integrability of a square billiard is spontaneously broken as it rotates
about one of its corners. The system becomes quasi-integrable where the
invariant tori are broken with respect to a certain parameter, where E is the energy of the particle inside the billiard and
is the angular frequency of rotation of billiard. We study the system
classically and quantum mechanically in view of obtaining a correspondence in
the two descriptions. Classical phase space in Poincar\'{e} surface of section
shows transition from regular to chaotic motion as the parameter is
decreased. In the Quantum counterpart, the spectral statistics shows a
transition from Poisson to Wigner distribution as the system turns chaotic with
decrease in . The wavefunction statistics however show breakdown of
time-reversal symmetry as decreases
A high-performance magnesium lattice clock: stability and accuracy analysis
Optical lattice clocks have reached uncertainties in 10^{-18} regime, well surpassing the primary microwave frequency standard. Such performance levels have allowed for applications from geodesy to fundamental physics. The performance of state of the art optical lattice clocks are strongly influenced by black body radiation (BBR) induced frequency shifts. Magnesium is one of the optical lattice clock candidate elements with very low sensitivity to BBR, which makes it an interesting candidate as an optical frequency reference.
Optical lattice clocks rely on high-Q optical transitions, where Doppler and recoil shifts are suppressed by trapping the atoms in Lamb-Dicke regime. For Magnesium, due to its low atomic mass, the tunneling induced line-broadening is significantly large. This has been a bottleneck in reducing the instability of Magnesium lattice clock. However the large tunneling rate for Magnesium atoms in the optical lattice also allows us to study these lattice effects using optical spectroscopy.
Lattice AC Stark shift is one of the important contributions to the uncertainty budget for an optical lattice clock. To achieve clock uncertainties in 10^{-18} regime, even the contributions from multipolar polarizabilities and hyperpolarizability becomes significant. Therefore, operational magic frequencies have been identified in Strontium and Ytterbium lattice clocks, where the light shift dependence on intensity is zero to the lowest order.
In this thesis, an extensive model has been developed to understand the influence of tunneling in a one dimensional optical lattice on the clock transition lineshape. This model is used to simulate the spectroscopy results previously observed in our experiment, which show strong lineshape asymmetry as lattice wavelength is detuned from the magic condition. The strong influence of transverse states in generating these asymmetries was highlighted by numerical simulations.
To improve the performance of our Magnesium lattice clock from the last frequency measurements, lattice system upgrades were carried out within the scope of this thesis. This allowed to suppress the tunneling induced line-broadening to sub-Hz regime for the first time for magnesium, and to resolve the 1S0-3P0 clock transition with a linewidth of 7(3) Hz. The high line-Q thus obtained of 9(3) x 10^{13} helped reduce the clock instability in self-comparison measurement to 7.2^{+7.7}_{-1.8} x 10^{-17} in 3000 seconds of averaging time.
The improved clock instability also helped estimate various systematic shifts with much improved uncertainties. The probe AC Stark shift and Zeeman shift uncertainties were reduced to the mid-10^{-17} regime, while cold collision shift was characterized with uncertainty of 1.4 x 10^{-16}. With an aim to similarly reduce lattice AC Stark shift uncertainty, influence of higher order shifts was characterized for Magnesium for the first time. The hyperpolarizability coefficient was estimated to be 197(53) micro Hz/(kWcm^{-2})^2. These measurements show that the lattice shift can be characterized with an uncertainty of 6.5 x 10^{-16}, paving way for a future frequency measurement with more than an order of magnitude lower uncertainty
Modeling Data Reuse in Deep Neural Networks by Taking Data-Types into Cognizance
In recent years, researchers have focused on reducing the model size and
number of computations (measured as "multiply-accumulate" or MAC operations) of
DNNs. The energy consumption of a DNN depends on both the number of MAC
operations and the energy efficiency of each MAC operation. The former can be
estimated at design time; however, the latter depends on the intricate data
reuse patterns and underlying hardware architecture. Hence, estimating it at
design time is challenging. This work shows that the conventional approach to
estimate the data reuse, viz. arithmetic intensity, does not always correctly
estimate the degree of data reuse in DNNs since it gives equal importance to
all the data types. We propose a novel model, termed "data type aware weighted
arithmetic intensity" (), which accounts for the unequal importance of
different data types in DNNs. We evaluate our model on 25 state-of-the-art DNNs
on two GPUs. We show that our model accurately models data-reuse for all
possible data reuse patterns for different types of convolution and different
types of layers. We show that our model is a better indicator of the energy
efficiency of DNNs. We also show its generality using the central limit
theorem.Comment: Accepted at IEEE Transactions on Computers (Special Issue on
Machine-Learning Architectures and Accelerators) 202
DeepReShape: Redesigning Neural Networks for Efficient Private Inference
Prior work on Private Inference (PI)--inferences performed directly on
encrypted input--has focused on minimizing a network's ReLUs, which have been
assumed to dominate PI latency rather than FLOPs. Recent work has shown that
FLOPs for PI can no longer be ignored and have high latency penalties. In this
paper, we develop DeepReShape, a network redesign technique that tailors
architectures to PI constraints, optimizing for both ReLUs and FLOPs for the
first time. The {\em key insight} is that a strategic allocation of channels
such that the network's ReLUs are aligned in their criticality order
simultaneously optimizes ReLU and FLOPs efficiency. DeepReShape automates
network development with an efficient process, and we call generated networks
HybReNets. We evaluate DeepReShape using standard PI benchmarks and demonstrate
a 2.1\% accuracy gain with a 5.2 runtime improvement at iso-ReLU on
CIFAR-100 and an 8.7 runtime improvement at iso-accuracy on
TinyImageNet. Furthermore, we demystify the input network selection in prior
ReLU optimizations and shed light on the key network attributes enabling PI
efficiency.Comment: 37 pages, 23 Figures, and 17 Table
Innovative techniques for deployment of microservices in cloud-edge environment
PhD ThesisThe evolution of microservice architecture allows complex applications to be structured
into independent modular components (microservices) making them easier to develop
and manage. Complemented with containers, microservices can be deployed across
any cloud and edge environment. Although containerized microservices are getting
popular in industry, less research is available specially in the area of performance
characterization and optimized deployment of microservices.
Depending on the application type (e.g. web, streaming) and the provided functionalities
(e.g. ltering, encryption/decryption, storage), microservices are heterogeneous
with speci c functional and Quality of Service (QoS) requirements. Further, cloud
and edge environments are also complex with a huge number of cloud providers and
edge devices along with their host con gurations. Due to these complexities, nding
a suitable deployment solution for microservices becomes challenging.
To handle the deployment of microservices in cloud and edge environments, this thesis
presents multilateral research towards microservice performance characterization,
run-time evaluation and system orchestration. Considering a variety of applications,
numerous algorithms and policies have been proposed, implemented and prototyped.
The main contributions of this thesis are given below:
Characterizes the performance of containerized microservices considering various
types of interference in the cloud environment.
Proposes and models an orchestrator, SDBO for benchmarking simple webapplication
microservices in a multi-cloud environment. SDBO is validated using
an e-commerce test web-application.
Proposes and models an advanced orchestrator, GeoBench for the deployment of
complex web-application microservices in a multi-cloud environment. GeoBench
is validated using a geo-distributed test web-application.
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Proposes and models a run-time deployment framework for distributed streaming
application microservices in a hybrid cloud-edge environment. The model is
validated using a real-world healthcare analytics use case for human activity
recognition.
E2GC: Energy-efficient Group Convolution in Deep Neural Networks
The number of groups () in group convolution (GConv) is selected to boost
the predictive performance of deep neural networks (DNNs) in a compute and
parameter efficient manner. However, we show that naive selection of in
GConv creates an imbalance between the computational complexity and degree of
data reuse, which leads to suboptimal energy efficiency in DNNs. We devise an
optimum group size model, which enables a balance between computational cost
and data movement cost, thus, optimize the energy-efficiency of DNNs. Based on
the insights from this model, we propose an "energy-efficient group
convolution" (E2GC) module where, unlike the previous implementations of GConv,
the group size () remains constant. Further, to demonstrate the efficacy of
the E2GC module, we incorporate this module in the design of MobileNet-V1 and
ResNeXt-50 and perform experiments on two GPUs, P100 and P4000. We show that,
at comparable computational complexity, DNNs with constant group size (E2GC)
are more energy-efficient than DNNs with a fixed number of groups (FGC). For
example, on P100 GPU, the energy-efficiency of MobileNet-V1 and ResNeXt-50 is
increased by 10.8% and 4.73% (respectively) when E2GC modules substitute the
FGC modules in both the DNNs. Furthermore, through our extensive
experimentation with ImageNet-1K and Food-101 image classification datasets, we
show that the E2GC module enables a trade-off between generalization ability
and representational power of DNN. Thus, the predictive performance of DNNs can
be optimized by selecting an appropriate . The code and trained models are
available at https://github.com/iithcandle/E2GC-release.Comment: Accepted as a conference paper in 2020 33rd International Conference
on VLSI Design and 2020 19th International Conference on Embedded Systems
(VLSID
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