Efficient audio signal processing for embedded systems

We investigated two design strategies that would allow us to efficiently process audio signals on embedded systems such as mobile phones and portable electronics. In the first strategy, we exploit properties of the human auditory system to process audio signals. We designed a sound enhancement algorithm to make piezoelectric loudspeakers sound "richer" and "fuller," using a combination of bass extension and dynamic range compression. We also developed an audio energy reduction algorithm for loudspeaker power management by suppressing signal energy below the masking threshold. In the second strategy, we use low-power analog circuits to process the signal before digitizing it. We designed an analog front-end for sound detection and implemented it on a field programmable analog array (FPAA). The sound classifier front-end can be used in a wide range of applications because programmable floating-gate transistors are employed to store classifier weights. Moreover, we incorporated a feature selection algorithm to simplify the analog front-end. A machine learning algorithm AdaBoost is used to select the most relevant features for a particular sound detection application. We also designed the circuits to implement the AdaBoost-based analog classifier.PhDCommittee Chair: Anderson, David; Committee Member: Hasler, Jennifer; Committee Member: Hunt, William; Committee Member: Lanterman, Aaron; Committee Member: Minch, Bradle

Configurable analog hardware for neuromorphic Bayesian inference and least-squares solutions

Sparse approximation is a Bayesian inference program with a wide number of signal processing applications, such as Compressed Sensing recovery used in medical imaging. Previous sparse coding implementations relied on digital algorithms whose power consumption and performance scale poorly with problem size, rendering them unsuitable for portable applications, and a bottleneck in high speed applications. A novel analog architecture, implementing the Locally Competitive Algorithm (LCA), was designed and programmed onto a Field Programmable Analog Arrays (FPAAs), using floating gate transistors to set the analog parameters. A network of 6 coefficients was demonstrated to converge to similar values as a digital sparse approximation algorithm, but with better power and performance scaling. A rate encoded spiking algorithm was then developed, which was shown to converge to similar values as the LCA. A second novel architecture was designed and programmed on an FPAA implementing the spiking version of the LCA with integrate and fire neurons. A network of 18 neurons converged on similar values as a digital sparse approximation algorithm, with even better performance and power efficiency than the non-spiking network. Novel algorithms were created to increase floating gate programming speed by more than two orders of magnitude, and reduce programming error from device mismatch. A new FPAA chip was designed and tested which allowed for rapid interfacing and additional improvements in accuracy. Finally, a neuromorphic chip was designed, containing 400 integrate and fire neurons, and capable of converging on a sparse approximation solution in 10 microseconds, over 1000 times faster than the best digital solution.Ph.D

Analog microelectronic emulation for dynamic power system computation

Power system dynamic simulators can be classified according to multiple criteria, including speed, precision, cost and modularity (topology, characteristics and model). Existing simulators are based on time-consuming numeric algorithms, which provide very precise results. But the evolution of the power grid constantly changes the requirements for simulators. In fact, power consumption is steadily increasing; therefore, the power system is always operating closer to its limits. Moreover, focus is put on decentralized and stochastic green energy sources, leading to a much more complex and less predictable power system. In order to guarantee security of supply under these conditions, real-time control and online security assessment are of the utmost importance. The main requirement for power system simulators in this context thus becomes the simulation time. The simulator has to be able to reproduce power system phenomena much faster than their real-time duration. An effective way to accelerate computation time of power system stability simulators is based on analog emulation of the power system grid. The idea is to avoid the heavy, time-consuming numerical matrix calculations of the grid by using an instantaneous analog Kirchhoff grid, with which computation becomes intrinsically parallel and the simulation time independent of the power system topology size. An overview of the power system computation history and the evolution of microelectronics highlights that the renaissance of dedicated analog computation is justified. Modern VLSI technologies can overcome the drawbacks which caused the disappearance of analog computation units in the 1960s. This work addresses therefore the development of a power system emulation approach from its theoretical principles to the behavioral design and the microelectronic implementation of a first demonstrator. The approach used in this research is called AC emulation approach and is based on a one-to-one mapping of components of the real power system (generator, load and transmission line) by emulating their behavior on a CMOS microelectronic integrated circuit (ASIC). The signals propagating on the emulated grid are the shrunk and downscaled current and voltage waves of the real power system. The uniqueness of this emulation approach is that frequency dependence of the signals is preserved. Therefore, the range of phenomena that can be emulated with an AC emulator depends only on the implemented models. Within the framework of this thesis, we restrict our developments to transient stability analysis, as our main focus is put on emulation speed. v We provide behavioral AC emulation models for the three main power system components. Thereby, special attention is paid to the generator model, which is shown to introduce a systematic error. This error is analyzed and reduced by model adaptation. Behavioral simulation results validate the developed models. Moreover, we suggest custom programmable analog building blocks for the implementation of the proposed behavioral models. During their design, application specific requirements, as well as imperfections, calibration, mismatch and process-variation aspects, are taken into account. In particular, the design of the tunable floating inductance used in all three AC emulation models is discussed in detail. In fact, major design challenges have to be addressed in order to achieve an inductance suitable for our application. Finally, a first AC emulation demonstrator is presented. A benchmark using a fixed two- machine topology has been implemented using a 0.35μm 3.3V CMOS technology. The characteristics of the emulated components (i.e. generators and transmission lines) are reprogrammable, allowing short circuits to be emulated at different distances from the generator. The emulated phenomena are shown to be 10′000 times faster than real time, therefore proving the high-speed capabilities of AC emulation

Design of a Multi-sensor and Re-configurable Smart Node for the IoT

The rapid deployment of the Internet of Things (IoT) is much dependent on the capacity of the IoT node to be able to self-adapt to the target application. With the increase of sensor networks and diversity of sensors available and with the increasing integration of multiple sensors in a sensor node, it is necessary to develop systems capable of handling all of these sensors with high level of flexibility. These may have different characteristics that provide quite distinct interface requirements, thus giving rise to the need for systems with re-configurable properties. With the implementation of sensor networks in places where energy supply is limited or non-existent, and in situations where technician intervention is expensive, there is a need to exchange conventional energy sources by methods of storage and harvesting of the energy present in the environment, where the sensor node is used (autonomous and renewable energy sources). This thesis will focus on the study and implementation of a family of re-configurable and multi-sensor IoT nodes with special emphasis on the energy storage and power management. It will also focus on the develop of a CAD tool in order to help in the design of CMOS circuits, for the purpose of integrating all the strategies here presented

Intrinsic Hardware Evolution on the Transistor Level

This thesis presents a novel approach to the automated synthesis of analog circuits. Evolutionary algorithms are used in conjunction with a fitness evaluation on a dedicated ASIC that serves as the analog substrate for the newly bred candidate solutions. The advantage of evaluating the candidate circuits directly in hardware is twofold. First, it may speed up the evolutionary algorithms, because hardware tests can usually be performed faster than simulations. Second, the evolved circuits are guaranteed to work on a real piece of silicon. The proposed approach is realized as a hardware evolution system consisting of an IBM compatible general purpose computer that hosts the evolutionary algorithm, an FPGA-based mixed signal test board, and the analog substrate. The latter one is designed as a Field Programmable Transistor Array (FPTA) whose programmable transistor cells can be almost freely connected. The transistor cells can be configured to adopt one out of 75 different channel geometries. The chip was produced in a 0.6µm CMOS process and provides ample means for the input and output of analog signals. The configuration is stored in SRAM cells embedded in the programmable transistor cells. The hardware evolution system is used for numerous evolution experiments targeted at a wide variety of different circuit functionalities. These comprise logic gates, Gaussian function circuits, D/A converters, low- and highpass filters, tone discriminators, and comparators. The experimental results are thoroughly analyzed and discussed with respect to related work

Low-power adaptive control scheme using switching activity measurement method for reconfigurable analog-to-digital converters

Power consumption is a critical issue for portable devices. The ever-increasing demand for multimode wireless applications and the growing concerns towards power-aware green technology make dynamically reconfigurable hardware an attractive solution for overcoming the power issue. This is due to its advantages of flexibility, reusability, and adaptability. During the last decade, reconfigurable analog-to-digital converters (ReADCs) have been used to support multimode wireless applications. With the ability to adaptively scale the power consumption according to different operation modes, reconfigurable devices utilise the power supply efficiently. This can prolong battery life and reduce unnecessary heat emission to the environment. However, current adaptive mechanisms for ReADCs rely upon external control signals generated using digital signal processors (DSPs) in the baseband. This thesis aims to provide a single-chip solution for real-time and low-power ReADC implementations that can adaptively change the converter resolution according to signal variations without the need of the baseband processing. Specifically, the thesis focuses on the analysis, design and implementation of a low-power digital controller unit for ReADCs. In this study, the following two important reconfigurability issues are investigated: i) the detection mechanism for an adaptive implementation, and ii) the measure of power and area overheads that are introduced by the adaptive control modules. This thesis outlines four main achievements to address these issues. The first achievement is the development of the switching activity measurement (SWAM) method to detect different signal components based upon the observation of the output of an ADC. The second achievement is a proposed adaptive algorithm for ReADCs to dynamically adjust the resolution depending upon the variations in the input signal. The third achievement is an ASIC implementation of the adaptive control module for ReADCs. The module achieves low reconfiguration overheads in terms of area and power compared with the main analog part of a ReADC. The fourth achievement is the development of a low-power noise detection module using a conventional ADC for signal improvement. Taken together, the findings from this study demonstrate the potential use of switching activity information of an ADC to adaptively control the circuits, and simultaneously expanding the functionality of the ADC in electronic systems

Articles indexats publicats per investigadors del Campus de Terrassa: 2013

Aquest informe recull els 228 treballs publicats per 177 investigadors/es del Campus de Terrassa en revistes indexades al Journal Citation Report durant el 2013Preprin

A Practical Investigation into Achieving Bio-Plausibility in Evo-Devo Neural Microcircuits Feasible in an FPGA

Many researchers has conjectured, argued, or in some cases demonstrated, that bio-plausibility can bring about emergent properties such as adaptability, scalability, fault-tolerance, self-repair, reliability, and autonomy to bio-inspired intelligent systems. Evolutionary-developmental (evo-devo) spiking neural networks are a very bio-plausible mixture of such bio-inspired intelligent systems that have been proposed and studied by a few researchers. However, the general trend is that the complexity and thus the computational cost grow with the bio-plausibility of the system. FPGAs (Field- Programmable Gate Arrays) have been used and proved to be one of the flexible and cost efficient hardware platforms for research' and development of such evo-devo systems. However, mapping a bio-plausible evo-devo spiking neural network to an FPGA is a daunting task full of different constraints and trade-offs that makes it, if not infeasible, very challenging. This thesis explores the challenges, trade-offs, constraints, practical issues, and some possible approaches in achieving bio-plausibility in creating evolutionary developmental spiking neural microcircuits in an FPGA through a practical investigation along with a series of case studies. In this study, the system performance, cost, reliability, scalability, availability, and design and testing time and complexity are defined as measures for feasibility of a system and structural accuracy and consistency with the current knowledge in biology as measures for bio-plausibility. Investigation of the challenges starts with the hardware platform selection and then neuron, cortex, and evo-devo models and integration of these models into a whole bio-inspired intelligent system are examined one by one. For further practical investigation, a new PLAQIF Digital Neuron model, a novel Cortex model, and a new multicellular LGRN evo-devo model are designed, implemented and tested as case studies. Results and their implications for the researchers, designers of such systems, and FPGA manufacturers are discussed and concluded in form of general trends, trade-offs, suggestions, and recommendations