2,292 research outputs found
XNOR Neural Engine: a Hardware Accelerator IP for 21.6 fJ/op Binary Neural Network Inference
Binary Neural Networks (BNNs) are promising to deliver accuracy comparable to
conventional deep neural networks at a fraction of the cost in terms of memory
and energy. In this paper, we introduce the XNOR Neural Engine (XNE), a fully
digital configurable hardware accelerator IP for BNNs, integrated within a
microcontroller unit (MCU) equipped with an autonomous I/O subsystem and hybrid
SRAM / standard cell memory. The XNE is able to fully compute convolutional and
dense layers in autonomy or in cooperation with the core in the MCU to realize
more complex behaviors. We show post-synthesis results in 65nm and 22nm
technology for the XNE IP and post-layout results in 22nm for the full MCU
indicating that this system can drop the energy cost per binary operation to
21.6fJ per operation at 0.4V, and at the same time is flexible and performant
enough to execute state-of-the-art BNN topologies such as ResNet-34 in less
than 2.2mJ per frame at 8.9 fps.Comment: 11 pages, 8 figures, 2 tables, 3 listings. Accepted for presentation
at CODES'18 and for publication in IEEE Transactions on Computer-Aided Design
of Circuits and Systems (TCAD) as part of the ESWEEK-TCAD special issu
Nucleon Spin Structure with hadronic collisions at COMPASS
In order to illustrate the capabilities of COMPASS using a hadronic beam, I
review some of the azimuthal asymmetries in hadronic collisions, that allow for
the extraction of transversity, Sivers and Boer-Mulders functions, necessary to
explore the partonic spin structure of the nucleon. I also report on some Monte
Carlo simulations of such asymmetries for the production of Drell-Yan lepton
pairs from the collision of high-energy pions on a transversely polarized
proton target.Comment: talk delivered to the "International Workshop on Structure and
Spectroscopy", Freiburg, March 19-21, 2007; 18 pages, RevTeX4 style, 8
figures with 10 .eps file
Chipmunk: A Systolically Scalable 0.9 mm, 3.08 Gop/s/mW @ 1.2 mW Accelerator for Near-Sensor Recurrent Neural Network Inference
Recurrent neural networks (RNNs) are state-of-the-art in voice
awareness/understanding and speech recognition. On-device computation of RNNs
on low-power mobile and wearable devices would be key to applications such as
zero-latency voice-based human-machine interfaces. Here we present Chipmunk, a
small (<1 mm) hardware accelerator for Long-Short Term Memory RNNs in UMC
65 nm technology capable to operate at a measured peak efficiency up to 3.08
Gop/s/mW at 1.24 mW peak power. To implement big RNN models without incurring
in huge memory transfer overhead, multiple Chipmunk engines can cooperate to
form a single systolic array. In this way, the Chipmunk architecture in a 75
tiles configuration can achieve real-time phoneme extraction on a demanding RNN
topology proposed by Graves et al., consuming less than 13 mW of average power
Helical axis analysis to quantify humeral kinematics during shoulder rotation.
© 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.Information pertaining to the helical axis during humeral kinematics during shoulder rotation may be of benefit to better understand conditions such as shoulder instability. The aim of this study is to quantify the behavior of humeral rotations using helical axis (HA) parameters in three different conditions. A total of 19 people without shoulder symptoms participated in the experiment. Shoulder kinematics was measured with an optoelectric motion capture system. The subjects performed three different full range rotations of the shoulder. The shoulder movements were analyzed with the HA technique. Four parameters were extracted from the HA of the shoulder during three different full-range rotations: range of movement (RoM), mean angle (MA), axis dispersion (MDD), and distance of their center from the shoulder (D). No significant differences were observed in the RoM for each condition between left and right side. The MA of the axis was significantly lower on the right side compared to the left in each of the three conditions. The MDD was also lower for the right side compared to the left side in each of the three conditions.The four parameters proposed for the analysis of shoulder kinematics showed to be promising indicators of shoulder instability.Peer reviewe
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Fully-autonomous miniaturized robots (e.g., drones), with artificial
intelligence (AI) based visual navigation capabilities are extremely
challenging drivers of Internet-of-Things edge intelligence capabilities.
Visual navigation based on AI approaches, such as deep neural networks (DNNs)
are becoming pervasive for standard-size drones, but are considered out of
reach for nanodrones with size of a few cm. In this work, we
present the first (to the best of our knowledge) demonstration of a navigation
engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based
visual navigation. To achieve this goal we developed a complete methodology for
parallel execution of complex DNNs directly on-bard of resource-constrained
milliwatt-scale nodes. Our system is based on GAP8, a novel parallel
ultra-low-power computing platform, and a 27 g commercial, open-source
CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the
software mapping techniques that enable the state-of-the-art deep convolutional
neural network presented in [1] to be fully executed on-board within a strict 6
fps real-time constraint with no compromise in terms of flight results, while
all processing is done with only 64 mW on average. Our navigation engine is
flexible and can be used to span a wide performance range: at its peak
performance corner it achieves 18 fps while still consuming on average just
3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication
in the IEEE Internet of Things Journal (IEEE IOTJ
A folded Fabry-Perot cavity for optical sensing in gravitational wave detectors
Abstract The sensitivity of standard optical schemes for the readout of weak vibrations is limited thermal and radiation pressure fluctuations induced by the small interrogation area. We propose and analyze an optical configuration allowing to overcome this problem and optimize the sensitivity of the new generation of massive gravitational wave detectors
A regional GIS-based model for reconstructing natural monthly streamflow series at ungauged sites
Several hydrologic applications require reliable estimates of monthly runoff in river basins to face the widespread
lack of data, both in time and in space. The main aim of this work is to propose a regional model for the estimation
of monthly natural runoff series at ungauged sites, analyzing its applicability, reliability and limitations.
A GIS (Geographic Information System) based model is here developed and applied to the entire region of Sicily
(Italy). The core of this tool is a regional model for the estimation of monthly natural runoff series, based on a
simple modelling structure, consisting of a regression based rainfall-runoff model with only four parameters. The
monthly runoff is obtained as a function of precipitation and mean temperature at the same month and runoff at
the previous month. For a given basin, the four model parameters are assessed by specific regional equations as a
function of some easily measurable geomorphic and climate basins’ descriptors.
The model is calibrated by a “two-step” procedure applied to a number of gauged basins over the region. The
first step is aimed at the identification of a set of parameters optimizing model performances at the level of single
basin. Such “optimal” parameters sets, derived for each calibration basin, are successively used inside a regional
regression analysis, performed at the second step, by which the regional equations for model parameters assessment
are defined and calibrated. All the gauged watersheds across the Sicily have been analyzed, selecting 53 basins for
model calibration and using other 6 basins exclusively for validation purposes. Model performances, quantitatively
evaluated considering different statistical indexes, demonstrate a relevant model ability in capturing the observed
hydrological response at both the monthly level and higher time scales (seasonal and annual).
One of the key features related to the proposed methodology is its easy transferability to other arid and semiarid
Mediterranean areas; thus, the application here shown may be considered as a benchmark for similar studies. The
calibrated model is implemented by a GIS software (i.e. Quantum GIS 2.10), automatizing data retrieving and
processing procedures and creating a prompt and reliable tool for filling/reconstructing precipitation, temperature
or streamflow time series at any gauged or ungauged Sicilian basin. The proposed GIS plug-in can, in fact, be
applied at any point of the hydrographical network of the region, assessing the precipitation, temperature and
natural streamflow series (at the monthly or higher time scales) for a desired time-window
Transverse-momentum distributions in a diquark spectator model
All the leading-twist parton distribution functions are calculated in a
spectator model of the nucleon, using scalar and axial-vector diquarks. Single
gluon rescattering is used to generate T-odd distribution functions. Different
choices for the diquark polarization states are considered, as well as a few
options for the form factor at the nucleon-quark-diquark vertex. The results
are listed in analytic form and interpreted in terms of light-cone wave
functions. The model parameters are fixed by reproducing the phenomenological
parametrization of unpolarized and helicity parton distributions at the lowest
available scale. Predictions for the other parton densities are given and,
whenever possible, compared with available phenomenological parametrizations.Comment: 42 pages, 13 figures in .eps format. RevTeX style. Minor typos
corrected, added one referenc
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