Enhancing Real-time Embedded Image Processing Robustness on Reconfigurable Devices for Critical Applications

Nowadays, image processing is increasingly used in several application fields, such as biomedical, aerospace, or automotive. Within these fields, image processing is used to serve both non-critical and critical tasks. As example, in automotive, cameras are becoming key sensors in increasing car safety, driving assistance and driving comfort. They have been employed for infotainment (non-critical), as well as for some driver assistance tasks (critical), such as Forward Collision Avoidance, Intelligent Speed Control, or Pedestrian Detection. The complexity of these algorithms brings a challenge in real-time image processing systems, requiring high computing capacity, usually not available in processors for embedded systems. Hardware acceleration is therefore crucial, and devices such as Field Programmable Gate Arrays (FPGAs) best fit the growing demand of computational capabilities. These devices can assist embedded processors by significantly speeding-up computationally intensive software algorithms. Moreover, critical applications introduce strict requirements not only from the real-time constraints, but also from the device reliability and algorithm robustness points of view. Technology scaling is highlighting reliability problems related to aging phenomena, and to the increasing sensitivity of digital devices to external radiation events that can cause transient or even permanent faults. These faults can lead to wrong information processed or, in the worst case, to a dangerous system failure. In this context, the reconfigurable nature of FPGA devices can be exploited to increase the system reliability and robustness by leveraging Dynamic Partial Reconfiguration features. The research work presented in this thesis focuses on the development of techniques for implementing efficient and robust real-time embedded image processing hardware accelerators and systems for mission-critical applications. Three main challenges have been faced and will be discussed, along with proposed solutions, throughout the thesis: (i) achieving real-time performances, (ii) enhancing algorithm robustness, and (iii) increasing overall system's dependability. In order to ensure real-time performances, efficient FPGA-based hardware accelerators implementing selected image processing algorithms have been developed. Functionalities offered by the target technology, and algorithm's characteristics have been constantly taken into account while designing such accelerators, in order to efficiently tailor algorithm's operations to available hardware resources. On the other hand, the key idea for increasing image processing algorithms' robustness is to introduce self-adaptivity features at algorithm level, in order to maintain constant, or improve, the quality of results for a wide range of input conditions, that are not always fully predictable at design-time (e.g., noise level variations). This has been accomplished by measuring at run-time some characteristics of the input images, and then tuning the algorithm parameters based on such estimations. Dynamic reconfiguration features of modern reconfigurable FPGA have been extensively exploited in order to integrate run-time adaptivity into the designed hardware accelerators. Tools and methodologies have been also developed in order to increase the overall system dependability during reconfiguration processes, thus providing safe run-time adaptation mechanisms. In addition, taking into account the target technology and the environments in which the developed hardware accelerators and systems may be employed, dependability issues have been analyzed, leading to the development of a platform for quickly assessing the reliability and characterizing the behavior of hardware accelerators implemented on reconfigurable FPGAs when they are affected by such faults

Using Fine Grain Approaches for highly reliable Design of FPGA-based Systems in Space

Nowadays using SRAM based FPGAs in space missions is increasingly considered due to their flexibility and reprogrammability. A challenge is the devices sensitivity to radiation effects that increased with modern architectures due to smaller CMOS structures. This work proposes fault tolerance methodologies, that are based on a fine grain view to modern reconfigurable architectures. The focus is on SEU mitigation challenges in SRAM based FPGAs which can result in crucial situations

Signaling in 3-D integrated circuits, benefits and challenges

Three-dimensional (3-D) or vertical integration is a design and packaging paradigm that can mitigate many of the increasing challenges related to the design of modern integrated systems. 3-D circuits have recently been at the spotlight, since these circuits provide a potent approach to enhance the performance and integrate diverse functions within amulti-plane stack. Clock networks consume a great portion of the power dissipated in a circuit. Therefore, designing a low-power clock network in synchronous circuits is an important task. This requirement is stricter for 3-D circuits due to the increased power densities. Synchronization issues can be more challenging for 3-D circuits since a clock path can spread across several planes with different physical and electrical characteristics. Consequently, designing low power clock networks for 3-D circuits is an important issue. Resonant clock networks are considered efficient low-power alternatives to conventional clock distribution schemes. These networks utilize additional inductive circuits to reduce power while delivering a full swing clock signal to the sink nodes. In this research, a design method to apply resonant clocking to synthesized clock trees is proposed. Manufacturing processes for 3-D circuits include some additional steps as compared to standard CMOS processes which makes 3-D circuits more susceptible to manufacturing defects and lowers the overall yield of the bonded 3-D stack. Testing is another complicated task for 3-D ICs, where pre-bond test is a prerequisite. Pre-bond testability, in turn, presents new challenges to 3-D clock network design primarily due to the incomplete clock distribution networks prior to the bonding of the planes. A design methodology of resonant 3-D clock networks that support wireless pre-bond testing is introduced. To efficiently address this issue, inductive links are exploited to wirelessly transmit the clock signal to the disjoint resonant clock networks. The inductors comprising the LC tanks are used as the receiver circuit for the links, essentially eliminating the need for additional circuits and/or interconnect resources during pre-bond test. Recent FPGAs are quite complex circuits which provide reconfigurablity at the cost of lower performance and higher power consumption as compared to ASIC circuits. Exploiting a large number of programmable switches, routing structures are mainly responsible for performance degradation in FPAGs. Employing 3-D technology can providemore efficient switches which drastically improve the performance and reduce the power consumption of the FPGA. RRAM switches are one of the most promising candidates to improve the FPGA routing architecture thanks to their low on-resistance and non-volatility. Along with the configurable switches, buffers are the other important element of the FPGAs routing structure. Different characteristics of RRAM switches change the properties of signal paths in RRAM-based FPGAs. The on resistance of RRAMswitches is considerably lower than CMOS pass gate switches which results in lower RC delay for RRAM-based routing paths. This different nature in critical path and signal delay in turn affect the need for intermediate buffers. Thus the buffer allocation should be reconsidered. In the last part of this research, the effect of intermediate buffers on signal propagation delay is studied and a modified buffer allocation scheme for RRAM-based FPGA routing path is proposed

Applications of reprogrammability in algorithm acceleration

This doctoral thesis consists of an introductory part and eight appended publications, which deal with hardware-based reprogrammability in algorithm acceleration with a specific emphasis on the possibilities offered by modern large-scale Field Programmable Gate Arrays (FPGAs) in computationally demanding applications. The historical evolution of both the theoretical and technological paths culminating in the introduction of reprogrammable logic devices is first outlined. This is followed by defining the commonly used terms in the thesis. The reprogrammable logic market is surveyed, and the architectural structures and the technological reasonings behind them are described in detail. As reprogrammable logic lies between Application Specific Integrated Circuits (ASICs) and general-purpose microprocessors in the implementation spectrum of electronics systems, special attention has been paid to differentiate these three implementation approaches. This has been done to emphasize, that reprogrammable logic offers much more than just a low-volume replacement for ASICs. Design systems for reprogrammable logic are investigated, as the learning curve associated with them is the main hurdle for software-oriented designers for using reprogrammable logic devices. The theoretically important topic of partial reprogrammability is described in detail, but it is concluded, that the practical problems in designing viable development platforms for partially reprogrammable systems will hinder its wide-spread adoption. The main technical, design-oriented, and economic applicability factors of reprogrammable logic are laid out. The main advantages of reprogrammable logic are their suitability for fine-grained bit-level parallelizable computing with a short time-to-market and low upfront costs. It is also concluded, that the main opportunities for reprogrammable logic lie in the potential of high-level design systems, and the ever-growing ASIC design gap. On the other hand, most power-conscious mass-market portable products do not seem to offer major new market potential for reprogrammable logic. The appended publications are examined and compared to contemporaneous research at other research institutions. The conclusion is that for relatively wide classes of well-defined computation problems, reprogrammable logic offers a more efficient solution than a software-centered approach, with a much shorter production cycle than is the case with ASICs.reviewe

Exploiting All-Programmable System on Chips for Closed-Loop Real-Time Neural Interfaces

High-density microelectrode arrays (HDMEAs) feature thousands of recording electrodes in a single chip with an area of few square millimeters. The obtained electrode density is comparable and even higher than the typical density of neuronal cells in cortical cultures. Commercially available HDMEA-based acquisition systems are able to record the neural activity from the whole array at the same time with submillisecond resolution. These devices are a very promising tool and are increasingly used in neuroscience to tackle fundamental questions regarding the complex dynamics of neural networks. Even if electrical or optical stimulation is generally an available feature of such systems, they lack the capability of creating a closed-loop between the biological neural activity and the artificial system. Stimuli are usually sent in an open-loop manner, thus violating the inherent working basis of neural circuits that in nature are constantly reacting to the external environment. This forbids to unravel the real mechanisms behind the behavior of neural networks. The primary objective of this PhD work is to overcome such limitation by creating a fullyreconfigurable processing system capable of providing real-time feedback to the ongoing neural activity recorded with HDMEA platforms. The potentiality of modern heterogeneous FPGAs has been exploited to realize the system. In particular, the Xilinx Zynq All Programmable System on Chip (APSoC) has been used. The device features reconfigurable logic, specialized hardwired blocks, and a dual-core ARM-based processor; the synergy of these components allows to achieve high elaboration performances while maintaining a high level of flexibility and adaptivity. The developed system has been embedded in an acquisition and stimulation setup featuring the following platforms: \u2022 3\ub7Brain BioCam X, a state-of-the-art HDMEA-based acquisition platform capable of recording in parallel from 4096 electrodes at 18 kHz per electrode. \u2022 PlexStim\u2122 Electrical Stimulator System, able to generate electrical stimuli with custom waveforms to 16 different output channels. \u2022 Texas Instruments DLP\uae LightCrafter\u2122 Evaluation Module, capable of projecting 608x684 pixels images with a refresh rate of 60 Hz; it holds the function of optical stimulation. All the features of the system, such as band-pass filtering and spike detection of all the recorded channels, have been validated by means of ex vivo experiments. Very low-latency has been achieved while processing the whole input data stream in real-time. In the case of electrical stimulation the total latency is below 2 ms; when optical stimuli are needed, instead, the total latency is a little higher, being 21 ms in the worst case. The final setup is ready to be used to infer cellular properties by means of closed-loop experiments. As a proof of this concept, it has been successfully used for the clustering and classification of retinal ganglion cells (RGCs) in mice retina. For this experiment, the light-evoked spikes from thousands of RGCs have been correctly recorded and analyzed in real-time. Around 90% of the total clusters have been classified as ON- or OFF-type cells. In addition to the closed-loop system, a denoising prototype has been developed. The main idea is to exploit oversampling techniques to reduce the thermal noise recorded by HDMEAbased acquisition systems. The prototype is capable of processing in real-time all the input signals from the BioCam X, and it is currently being tested to evaluate the performance in terms of signal-to-noise-ratio improvement

Regular Datapaths on Field-Programmable Gate Arrays

Field-Programmable Gate Arrays (FPGAs) are a recent kind of programmable logic device. They allow the implementation of integrated digital electronic circuits without requiring the complex optical, chemical and mechanical processes used in a conventional chip fabrication. FPGAs can be embedded in traditional system designflows to perform prototyping and emulation tasks. In addition, they also enable novel applications such as configurable computers with hardware dynamically adaptable to a specific problem. The growing chip capacity now allows even the implementation of CPUs and DSPs on single FPGAs. However, current design automation tools trace their roots to times of very limited FPGA sizes, and are primarily optimized for the implementation of random glue logic. The wide datapaths common to CPUs and DSPs are only processed with reduced performance. This thesis presents Structured Design Implementation (SDI), a suite of specialized tools coordinated by a common strategy, which aims to efficiently map even larger regular datapaths to FPGAs. In all steps, regularity is preserved whenever possible, or restored after disruptive operations were required. The circuits are composed from parametrizable modules providing a variety of logical, arithmetical and storage functions. For each module, multiple target FPGA-specific implementation alternatives may be generated in both gatelevel netlist and layout views. A floorplanner based on a genetic algorithm is then used to simultaneously choose an actual implementation from the set of alternatives for each module, and to arrange the selected module implementations in a linear placement. The floorplanning operation optimizes for short routing delays, high routability, and fit into the target FPGA.Field-Programmable Gate-Arrays (FPGAs) sind eine noch junge Art von programmierbaren Logikbausteinen. Sie erlauben die Implementierung von integrierten Digitalschaltungen ohne die komplizierten optischen, chemischen und mechanischen Prozesse, die normalerweise für die Chipfertigung erforderlich sind. FPGAs können im Rahmen konventioneller Entwurfsmethoden zu Emulationszwecken und Prototyp-Aufbauten herangezogen werden. Sie erlauben aber auch völlig neue Anwendungen wie rekonfigurierbare Computer, deren Hardware dynamisch an ein spezielles Problem angepaßt werden kann. Die gewachsene Chip-Kapazität erlaubt nun sogar die Implementierung von CPUs und digitalen Signalprozessoren (DSPs) auf einem einzelnen FPGA. Die Leistungsfähigkeit der entstandenen Schaltungen wird jedoch durch die zur Zeit erhältlichen CAD-Werkzeuge limitiert, da diese noch auf stark beschränkte FPGA-Größen ausgerichtet sind und primär der platzsparenden Verarbeitung unregelmäßiger Logik dienen. Die breiten Datenpfade in Bit-Slice-Struktur, die den Kern vieler CPUs und DSPs darstellen, werden nur suboptimal behandelt. Diese Arbeit stellt Structured Design Implementation (SDI) vor, ein System von spezialisierten CAD-Werkzeugen, die auch größere reguläre Datenpfade effizient auf FPGAs abbilden. In allen Verarbeitungsschritten wird dabei die bestehende Regularität soweit wie möglich erhalten oder nach regularitätsvernichtenden Operationen wiederhergestellt. Zur Schaltungseingabe steht eine Bibliothek von allgemeinen Modulen aus den Bereichen Logik, Arithmetik und Speicherung bereit. Diese können durch Belegung verschiedener Parameter wie Bit-Breiten und Datentypen an aktuelle Anforderungen angepaßt werden