FPGA Implementation of Convolutional Neural Networks with Fixed-Point Calculations

Neural network-based methods for image processing are becoming widely used in practical applications. Modern neural networks are computationally expensive and require specialized hardware, such as graphics processing units. Since such hardware is not always available in real life applications, there is a compelling need for the design of neural networks for mobile devices. Mobile neural networks typically have reduced number of parameters and require a relatively small number of arithmetic operations. However, they usually still are executed at the software level and use floating-point calculations. The use of mobile networks without further optimization may not provide sufficient performance when high processing speed is required, for example, in real-time video processing (30 frames per second). In this study, we suggest optimizations to speed up computations in order to efficiently use already trained neural networks on a mobile device. Specifically, we propose an approach for speeding up neural networks by moving computation from software to hardware and by using fixed-point calculations instead of floating-point. We propose a number of methods for neural network architecture design to improve the performance with fixed-point calculations. We also show an example of how existing datasets can be modified and adapted for the recognition task in hand. Finally, we present the design and the implementation of a floating-point gate array-based device to solve the practical problem of real-time handwritten digit classification from mobile camera video feed

Beam and SKS spectrometers at the K1.8 beam line

High-resolution spectrometers for both incident beams and scattered particles have been constructed at the K1.8 beam line of the Hadron Experimental Facility at J-PARC. A point-to-point optics is realized between the entrance and exit of QQDQQ magnets for the beam spectrometer. Fine-pitch wire chamber trackers and hodoscope counters are installed in the beam spectrometer to accept a high rate beam up to 107 Hz. The superconducting kaon spectrometer for scattered particles was transferred from KEK with modifications to the cryogenic system and detectors. A missing-mass resolution of 1.9 ± 0.1 MeV/c2 (FWHM) was achieved for the ∑ peaks of (π±, K+) reactions on a proton target in the first physics run of E19 in 2010

Phase Equilibria of the In–Pd–Sn System at 500 °C and 800 °C: Experimental Study and CALPHAD Modeling

Phase equilibria in the In–Pd–Sn system were investigated by a combination of key experiments and thermodynamic modeling. Partial isothermal sections at 500 °C and 800 °C of the In–Pd–Sn system for Pd contents above 66 at.% have been plotted experimentally using scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM/EDX) and X-ray diffraction (XRD). The solubility of the third component in binary compounds InPd3 and Pd3Sn was determined. The new ternary compound τ1 was found in Pd contents ranging from 20 to 25 at.% and at Sn contents varying from 5 to approximately 17 at.% Sn. This compound crystallizes in an Al3Ti-type tetragonal structure. Isostructural InPd2 and Pd2Sn phases from the In–Pd and Pd–Sn binary compositions form a continuous phase field in the ternary system at both temperatures. The temperatures of the solidus, liquidus, and phase transitions of the alloys along the Pd–In50Sn50 line were measured using DTA/DSC. Thermodynamic calculation of the In–Pd–Sn ternary system is performed using the CALPHAD method using the Thermo-Calc® software. The thermodynamic properties of the disordered fcc and liquid phases were described by the Redlich–Kister–Muggianu model. To describe intermetallic phases, namely, InPd3, Pd3Sn, τ1 and Pd2(InxSn1−x), a two-sublattice models was used. Thermodynamic description of the In–Pd–Sn system obtained in this study is in good agreement both with our results and the published experimental dat