72 research outputs found

    Fundamentals

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    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

    Understanding Quantum Technologies 2022

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    Understanding Quantum Technologies 2022 is a creative-commons ebook that provides a unique 360 degrees overview of quantum technologies from science and technology to geopolitical and societal issues. It covers quantum physics history, quantum physics 101, gate-based quantum computing, quantum computing engineering (including quantum error corrections and quantum computing energetics), quantum computing hardware (all qubit types, including quantum annealing and quantum simulation paradigms, history, science, research, implementation and vendors), quantum enabling technologies (cryogenics, control electronics, photonics, components fabs, raw materials), quantum computing algorithms, software development tools and use cases, unconventional computing (potential alternatives to quantum and classical computing), quantum telecommunications and cryptography, quantum sensing, quantum technologies around the world, quantum technologies societal impact and even quantum fake sciences. The main audience are computer science engineers, developers and IT specialists as well as quantum scientists and students who want to acquire a global view of how quantum technologies work, and particularly quantum computing. This version is an extensive update to the 2021 edition published in October 2021.Comment: 1132 pages, 920 figures, Letter forma

    The BrightEyes-TTM: an open-source time-tagging module for fluorescence lifetime imaging microscopy applications

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    The aim of this Ph.D. work is to reason and show how an open-source multi-channel and standalone time-tagging device was developed, validated and used in combination with a new generation of single-photon array detectors to pursue super-resolved time-resolved fluorescence lifetime imaging measurements. Within the compound of time-resolved fluorescence laser scanning microscopy (LSM) techniques, fluorescence lifetime imaging microscopy (FLIM) plays a relevant role in the life-sciences field, thanks to its ability of detecting functional changes within the cellular micro-environment. The recent advancements in photon detection technologies, such as the introduction of asynchronous read-out single-photon avalanche diode (SPAD) array detectors, allow to image a fluorescent sample with spatial resolution below the diffraction limit, at the same time, yield the possibility of accessing the single-photon information content allowing for time-resolved FLIM measurements. Thus, super-resolved FLIM experiments can be accomplished using SPAD array detectors in combination with pulsed laser sources and special data acquisition systems (DAQs), capable of handling a multiplicity of inputs and dealing with the single-photons readouts generated by SPAD array detectors. Nowadays, the commercial market lacks a true standalone, multi-channel, single-board, time-tagging and affordable DAQ device specifically designed for super-resolved FLIM experiments. Moreover, in the scientific community, no-efforts have been placed yet in building a device that can compensate such absence. That is why, within this Ph.D. project, an open-source and low-cost device, the so-called BrightEyes-TTM (time tagging module), was developed and validated both for fluorescence lifetime and time-resolved measurements in general. The BrightEyes-TTM belongs to a niche of DAQ devices called time-to-digital converters (TDCs). The field-gate programmable array (FPGA) technology was chosen for implementing the BrightEyes-TTM thanks to its reprogrammability and low cost features. The literature reports several different FPGA-based TDC architectures. Particularly, the differential delay-line TDC architecture turned out to be the most suitable for this Ph.D. project as it offers an optimal trade-off between temporal precision, temporal range, temporal resolution, dead-time, linearity, and FPGA resources, which are all crucial characteristics for a TDC device. The goal of the project of pursuing a cost-effective and further-upgradable open-source time-tagging device was achieved as the BrigthEyes-TTM was developed and assembled using low-cost commercially available electronic development kits, thus allowing for the architecture to be easily reproduced. BrightEyes-TTM was deployed on a FPGA development board which was equipped with a USB 3.0 chip for communicating with a host-processing unit and a multi-input/output custom-built interface card for interconnecting the TTM with the outside world. Licence-free softwares were used for acquiring, reconstructing and analyzing the BrightEyes-TTM time-resolved data. In order to characterize the BrightEyes-TTM performances and, at the same time, validate the developed multi-channel TDC architecture, the TTM was firstly tested on a bench and then integrated into a fluorescent LSM system. Yielding a 30 ps single-shot precision and linearity performances that allows to be employed for actual FLIM measurements, the BrightEyes-TTM, which also proved to acquire data from many channels in parallel, was ultimately used with a SPAD array detector to perform fluorescence imaging and spectroscopy on biological systems. As output of the Ph.D. work, the BrightEyes-TTM was released on GitHub as a fully open-source project with two aims. The principal aim is to give to any microscopy and life science laboratory the possibility to implement and further develop single-photon-based time-resolved microscopy techniques. The second aim is to trigger the interest of the microscopy community, and establish the BrigthEyes-TTM as a new standard for single-photon FLSM and FLIM experiments

    Low hardware consumption, resolution-configurable gray code oscillator time-to- digital converters implemented in 16nm, 20nm and 28nm FPGAs

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    This paper presents a low-hardware consumption, resolution-configurable, automatically calibrating gray code oscillator time-to-digital converter (GCO-TDC) in Xilinx 16nm UltraScale+, 20nm UltraScale and 28nm Virtex-7 field-programmable gate arrays (FPGAs). The proposed TDC utilizes LUTs as delay elements and has several innovations: 1) a sampling matrix structure to improve the resolution. 2) a virtual bin calibration method (VBCM) to achieve configurable resolutions and automatic calibration. 3) hardware implementation of the VBCM in standard FPGA devices. We implemented and evaluated a 16-channel TDC system in all three FPGAs. The UltraScale+ version achieved the best resolution (least significant bit, LSB) of 20.97 ps with 0.09 LSB averaged peak-to-peak differential nonlinearity (DNLpk-pk). The UltraScale and Virtex-7 versions achieved the best resolutions of 36.01 ps with 0.10 LSB averaged DNLpk-pk and 34.84 ps with 0.08 LSB averaged DNLpk-pk, respectively

    A 7.4-Bit ENOB 600 MS/s FPGA-Based Online Calibrated Slope ADC without External Components

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    A slope analog-to-digital converter (ADC) amenable to be fully implemented on a digital field programmable gate array (FPGA) without requiring any external active or passive components is proposed in this paper. The amplitude information, encoded in the transition times of a standard LVDS differential input—driven by the analog input and by the reference slope generated by an FPGA output buffer—is retrieved by an FPGA time-to-digital converter. Along with the ADC, a new online calibration algorithm is developed to mitigate the influence of process, voltage, and temperature variations on its performance. Measurements on an ADC prototype reveal an analog input range from 0.3 V to 1.5 V, a least significant bit (LSB) of 2.6 mV, and an effective number of bits (ENOB) of 7.4-bit at 600 MS/s. The differential nonlinearity (DNL) is in the range between −0.78 and 0.70 LSB, and the integral nonlinearity (INL) is in the range from −0.72 to 0.78 LSB

    Towards a fully integrated quantum optic circuit

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