Baseband analog front-end and digital back-end for reconfigurable multi-standard terminals

Multimedia applications are driving wireless network operators to add high-speed data services such as Edge (E-GPRS), WCDMA (UMTS) and WLAN (IEEE 802.11a,b,g) to the existing GSM network. This creates the need for multi-mode cellular handsets that support a wide range of communication standards, each with a different RF frequency, signal bandwidth, modulation scheme etc. This in turn generates several design challenges for the analog and digital building blocks of the physical layer. In addition to the above-mentioned protocols, mobile devices often include Bluetooth, GPS, FM-radio and TV services that can work concurrently with data and voice communication. Multi-mode, multi-band, and multi-standard mobile terminals must satisfy all these different requirements. Sharing and/or switching transceiver building blocks in these handsets is mandatory in order to extend battery life and/or reduce cost. Only adaptive circuits that are able to reconfigure themselves within the handover time can meet the design requirements of a single receiver or transmitter covering all the different standards while ensuring seamless inter-interoperability. This paper presents analog and digital base-band circuits that are able to support GSM (with Edge), WCDMA (UMTS), WLAN and Bluetooth using reconfigurable building blocks. The blocks can trade off power consumption for performance on the fly, depending on the standard to be supported and the required QoS (Quality of Service) leve

Analog Reconfigurable Circuits

The aim of this paper is to present an overview of a new branch of analog electronics represented by analog reconfigurable circuits. The reconfiguration of analog circuits has been known and used since the beginnings of electronics, but the universal reconfigurable circuits called Field Programmable Analog Arrays (FPAA) have been developed over the last two decades. This paper presents the classification of analog circuit reconfiguration, examples of FPAA solutions obtained as academic projects and commercially available ones, as well as some application examples of the dynamic reconfiguration of FPAA.

Digital Pulse Width Modulator Techniques For Dc - Dc Converters

Recent research activities focused on improving the steady-state as well as the dynamic behavior of DC-DC converters for proper system performance, by proposing different design methods and control approaches with growing tendency to using digital implementation over analog practices. Because of the rapid advancement in semiconductors and microprocessor industry, digital control grew in popularity among PWM converters and is taking over analog techniques due to availability of fast speed microprocessors, flexibility and immunity to noise and environmental variations. Furthermore, increased interest in Field Programmable Gate Arrays (FPGA) makes it a convenient design platform for digitally controlled converters. The objective of this research is to propose new digital control schemes, aiming to improve the steady-state and transient responses of a high switching frequency FPGA-based digitally controlled DC-DC converters. The target is to achieve enhanced performance in terms of tight regulation with minimum power consumption and high efficiency at steady-state, as well as shorter settling time with optimal over- and undershoots during transients. The main task is to develop new and innovative digital PWM techniques in order to achieve: 1. Tight regulation at steady-state: by proposing high resolution DPWM architecture, based on Digital Clock Management (DCM) resources available on FPGA boards. The proposed architecture Window-Masked Segmented Digital Clock Manager-FPGA based Digital Pulse Width Modulator Technique, is designed to achieve high resolution operating at high switching frequencies with minimum power consumption. 2. Enhanced dynamic response: by applying a shift to the basic saw-tooth DPWM signal, in order to benefit from the best linearity and simplest architecture offered by the conventional counter-comparator DPWM. This proposed control scheme will help the compensator reach the steady-state value faster. Dynamically Shifted Ramp Digital Control Technique for Improved Transient Response in DC-DC Converters, is projected to enhance the transient response by dynamically controlling the ramp signal of the DPWM unit

Reconfigurable Architectures and Systems for IoT Applications

abstract: Internet of Things (IoT) has become a popular topic in industry over the recent years, which describes an ecosystem of internet-connected devices or things that enrich the everyday life by improving our productivity and efficiency. The primary components of the IoT ecosystem are hardware, software and services. While the software and services of IoT system focus on data collection and processing to make decisions, the underlying hardware is responsible for sensing the information, preprocess and transmit it to the servers. Since the IoT ecosystem is still in infancy, there is a great need for rapid prototyping platforms that would help accelerate the hardware design process. However, depending on the target IoT application, different sensors are required to sense the signals such as heart-rate, temperature, pressure, acceleration, etc., and there is a great need for reconfigurable platforms that can prototype different sensor interfacing circuits. This thesis primarily focuses on two important hardware aspects of an IoT system: (a) an FPAA based reconfigurable sensing front-end system and (b) an FPGA based reconfigurable processing system. To enable reconfiguration capability for any sensor type, Programmable ANalog Device Array (PANDA), a transistor-level analog reconfigurable platform is proposed. CAD tools required for implementation of front-end circuits on the platform are also developed. To demonstrate the capability of the platform on silicon, a small-scale array of 24×25 PANDA cells is fabricated in 65nm technology. Several analog circuit building blocks including amplifiers, bias circuits and filters are prototyped on the platform, which demonstrates the effectiveness of the platform for rapid prototyping IoT sensor interfaces. IoT systems typically use machine learning algorithms that run on the servers to process the data in order to make decisions. Recently, embedded processors are being used to preprocess the data at the energy-constrained sensor node or at IoT gateway, which saves considerable energy for transmission and bandwidth. Using conventional CPU based systems for implementing the machine learning algorithms is not energy-efficient. Hence an FPGA based hardware accelerator is proposed and an optimization methodology is developed to maximize throughput of any convolutional neural network (CNN) based machine learning algorithm on a resource-constrained FPGA.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Data Conversion Within Energy Constrained Environments

Within scientific research, engineering, and consumer electronics, there is a multitude of new discrete sensor-interfaced devices. Maintaining high accuracy in signal quantization while staying within the strict power-budget of these devices is a very challenging problem. Traditional paths to solving this problem include researching more energy-efficient digital topologies as well as digital scaling.;This work offers an alternative path to lower-energy expenditure in the quantization stage --- content-dependent sampling of a signal. Instead of sampling at a constant rate, this work explores techniques which allow sampling based upon features of the signal itself through the use of application-dependent analog processing. This work presents an asynchronous sampling paradigm, based off the use of floating-gate-enabled analog circuitry. The basis of this work is developed through the mathematical models necessary for asynchronous sampling, as well the SPICE-compatible models necessary for simulating floating-gate enabled analog circuitry. These base techniques and circuitry are then extended to systems and applications utilizing novel analog-to-digital converter topologies capable of leveraging the non-constant sampling rates for significant sample and power savings

RF SDR for Wideband PMR

TErrestrial Trunked Radio (TETRA) offers capabilities equivalent to the second generation of mobile phones with voice and limited data capabilities. TETRA needs to evolve to satisfy increasing user demand for new services and facilities as well as gleaning the benefits of new technology. An initial enhancement (TETRA Enhanced Data Service, TEDS) has been agreed. The enhanced TETRA services allows for more flexibility in the communication modes used, so as to provide adaptability in applications. We propose that it is possible to deploy Software Defined Radio (SDR) technologies into the basestation to economically provide this level of flexibility and to further extend the capability of TETRA services by deploying a WiMAX channel into the proposed TETRA tuning range. Thus delivering true broadband data service while simultaneously supporting the original and enhanced TETRA services

Principles of Neuromorphic Photonics

In an age overrun with information, the ability to process reams of data has become crucial. The demand for data will continue to grow as smart gadgets multiply and become increasingly integrated into our daily lives. Next-generation industries in artificial intelligence services and high-performance computing are so far supported by microelectronic platforms. These data-intensive enterprises rely on continual improvements in hardware. Their prospects are running up against a stark reality: conventional one-size-fits-all solutions offered by digital electronics can no longer satisfy this need, as Moore's law (exponential hardware scaling), interconnection density, and the von Neumann architecture reach their limits. With its superior speed and reconfigurability, analog photonics can provide some relief to these problems; however, complex applications of analog photonics have remained largely unexplored due to the absence of a robust photonic integration industry. Recently, the landscape for commercially-manufacturable photonic chips has been changing rapidly and now promises to achieve economies of scale previously enjoyed solely by microelectronics. The scientific community has set out to build bridges between the domains of photonic device physics and neural networks, giving rise to the field of \emph{neuromorphic photonics}. This article reviews the recent progress in integrated neuromorphic photonics. We provide an overview of neuromorphic computing, discuss the associated technology (microelectronic and photonic) platforms and compare their metric performance. We discuss photonic neural network approaches and challenges for integrated neuromorphic photonic processors while providing an in-depth description of photonic neurons and a candidate interconnection architecture. We conclude with a future outlook of neuro-inspired photonic processing.Comment: 28 pages, 19 figure