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 mm2{}^2, 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${}^2$) 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${}^\mathrm{2}$. 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