Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification

Deep-learning is a cutting edge theory that is being applied to many fields. For vision applications the Convolutional Neural Networks (CNN) are demanding significant accuracy for classification tasks. Numerous hardware accelerators have populated during the last years to improve CPU or GPU based solutions. This technology is commonly prototyped and tested over FPGAs before being considered for ASIC fabrication for mass production. The use of commercial typical cameras (30fps) limits the capabilities of these systems for high speed applications. The use of dynamic vision sensors (DVS) that emulate the behavior of a biological retina is taking an incremental importance to improve this applications due to its nature, where the information is represented by a continuous stream of spikes and the frames to be processed by the CNN are constructed collecting a fixed number of these spikes (called events). The faster an object is, the more events are produced by DVS, so the higher is the equivalent frame rate. Therefore, these DVS utilization allows to compute a frame at the maximum speed a CNN accelerator can offer. In this paper we present a VHDL/HLS description of a pipelined design for FPGA able to collect events from an Address-Event-Representation (AER) DVS retina to obtain a normalized histogram to be used by a particular CNN accelerator, called NullHop. VHDL is used to describe the circuit, and HLS for computation blocks, which are used to perform the normalization of a frame needed for the CNN. Results outperform previous implementations of frames collection and normalization using ARM processors running at 800MHz on a Zynq7100 in both latency and power consumption. A measured 67% speedup factor is presented for a Roshambo CNN real-time experiment running at 160fps peak rate.Comment: 7 page

Exploring Processor and Memory Architectures for Multimedia

Multimedia has become one of the cornerstones of our 21st century society and, when combined with mobility, has enabled a tremendous evolution of our society. However, joining these two concepts introduces many technical challenges. These range from having sufficient performance for handling multimedia content to having the battery stamina for acceptable mobile usage. When taking a projection of where we are heading, we see these issues becoming ever more challenging by increased mobility as well as advancements in multimedia content, such as introduction of stereoscopic 3D and augmented reality. The increased performance needs for handling multimedia come not only from an ongoing step-up in resolution going from QVGA (320x240) to Full HD (1920x1080) a 27x increase in less than half a decade. On top of this, there is also codec evolution (MPEG-2 to H.264 AVC) that adds to the computational load increase. To meet these performance challenges there has been processing and memory architecture advances (SIMD, out-of-order superscalarity, multicore processing and heterogeneous multilevel memories) in the mobile domain, in conjunction with ever increasing operating frequencies (200MHz to 2GHz) and on-chip memory sizes (128KB to 2-3MB). At the same time there is an increase in requirements for mobility, placing higher demands on battery-powered systems despite the steady increase in battery capacity (500 to 2000mAh). This leaves negative net result in-terms of battery capacity versus performance advances. In order to make optimal use of these architectural advances and to meet the power limitations in mobile systems, there is a need for taking an overall approach on how to best utilize these systems. The right trade-off between performance and power is crucial. On top of these constraints, the flexibility aspects of the system need to be addressed. All this makes it very important to reach the right architectural balance in the system. The first goal for this thesis is to examine multimedia applications and propose a flexible solution that can meet the architectural requirements in a mobile system. Secondly, propose an automated methodology of optimally mapping multimedia data and instructions to a heterogeneous multilevel memory subsystem. The proposed methodology uses constraint programming for solving a multidimensional optimization problem. Results from this work indicate that using today’s most advanced mobile processor technology together with a multi-level heterogeneous on-chip memory subsystem can meet the performance requirements for handling multimedia. By utilizing the automated optimal memory mapping method presented in this thesis lower total power consumption can be achieved, whilst performance for multimedia applications is improved, by employing enhanced memory management. This is achieved through reduced external accesses and better reuse of memory objects. This automatic method shows high accuracy, up to 90%, for predicting multimedia memory accesses for a given architecture

System-on-chip Computing and Interconnection Architectures for Telecommunications and Signal Processing

This dissertation proposes novel architectures and design techniques targeting SoC building blocks for telecommunications and signal processing applications. Hardware implementation of Low-Density Parity-Check decoders is approached at both the algorithmic and the architecture level. Low-Density Parity-Check codes are a promising coding scheme for future communication standards due to their outstanding error correction performance. This work proposes a methodology for analyzing effects of finite precision arithmetic on error correction performance and hardware complexity. The methodology is throughout employed for co-designing the decoder. First, a low-complexity check node based on the P-output decoding principle is designed and characterized on a CMOS standard-cells library. Results demonstrate implementation loss below 0.2 dB down to BER of 10^{-8} and a saving in complexity up to 59% with respect to other works in recent literature. High-throughput and low-latency issues are addressed with modified single-phase decoding schedules. A new "memory-aware" schedule is proposed requiring down to 20% of memory with respect to the traditional two-phase flooding decoding. Additionally, throughput is doubled and logic complexity reduced of 12%. These advantages are traded-off with error correction performance, thus making the solution attractive only for long codes, as those adopted in the DVB-S2 standard. The "layered decoding" principle is extended to those codes not specifically conceived for this technique. Proposed architectures exhibit complexity savings in the order of 40% for both area and power consumption figures, while implementation loss is smaller than 0.05 dB. Most modern communication standards employ Orthogonal Frequency Division Multiplexing as part of their physical layer. The core of OFDM is the Fast Fourier Transform and its inverse in charge of symbols (de)modulation. Requirements on throughput and energy efficiency call for FFT hardware implementation, while ubiquity of FFT suggests the design of parametric, re-configurable and re-usable IP hardware macrocells. In this context, this thesis describes an FFT/IFFT core compiler particularly suited for implementation of OFDM communication systems. The tool employs an accuracy-driven configuration engine which automatically profiles the internal arithmetic and generates a core with minimum operands bit-width and thus minimum circuit complexity. The engine performs a closed-loop optimization over three different internal arithmetic models (fixed-point, block floating-point and convergent block floating-point) using the numerical accuracy budget given by the user as a reference point. The flexibility and re-usability of the proposed macrocell are illustrated through several case studies which encompass all current state-of-the-art OFDM communications standards (WLAN, WMAN, xDSL, DVB-T/H, DAB and UWB). Implementations results are presented for two deep sub-micron standard-cells libraries (65 and 90 nm) and commercially available FPGA devices. Compared with other FFT core compilers, the proposed environment produces macrocells with lower circuit complexity and same system level performance (throughput, transform size and numerical accuracy). The final part of this dissertation focuses on the Network-on-Chip design paradigm whose goal is building scalable communication infrastructures connecting hundreds of core. A low-complexity link architecture for mesochronous on-chip communication is discussed. The link enables skew constraint looseness in the clock tree synthesis, frequency speed-up, power consumption reduction and faster back-end turnarounds. The proposed architecture reaches a maximum clock frequency of 1 GHz on 65 nm low-leakage CMOS standard-cells library. In a complex test case with a full-blown NoC infrastructure, the link overhead is only 3% of chip area and 0.5% of leakage power consumption. Finally, a new methodology, named metacoding, is proposed. Metacoding generates correct-by-construction technology independent RTL codebases for NoC building blocks. The RTL coding phase is abstracted and modeled with an Object Oriented framework, integrated within a commercial tool for IP packaging (Synopsys CoreTools suite). Compared with traditional coding styles based on pre-processor directives, metacoding produces 65% smaller codebases and reduces the configurations to verify up to three orders of magnitude

Homogeneous and heterogeneous MPSoC architectures with network-on-chip connectivity for low-power and real-time multimedia signal processing

Two multiprocessor system-on-chip (MPSoC) architectures are proposed and compared in the paper with reference to audio and video processing applications. One architecture exploits a homogeneous topology; it consists of 8 identical tiles, each made of a 32-bit RISC core enhanced by a 64-bit DSP coprocessor with local memory. The other MPSoC architecture exploits a heterogeneous-tile topology with on-chip distributed memory resources; the tiles act as application specific processors supporting a different class of algorithms. In both architectures, the multiple tiles are interconnected by a network-on-chip (NoC) infrastructure, through network interfaces and routers, which allows parallel operations of the multiple tiles. The functional performances and the implementation complexity of the NoC-based MPSoC architectures are assessed by synthesis results in submicron CMOS technology. Among the large set of supported algorithms, two case studies are considered: the real-time implementation of an H.264/MPEG AVC video codec and of a low-distortion digital audio amplifier. The heterogeneous architecture ensures a higher power efficiency and a smaller area occupation and is more suited for low-power multimedia processing, such as in mobile devices. The homogeneous scheme allows for a higher flexibility and easier system scalability and is more suited for general-purpose DSP tasks in power-supplied devices

Design of multimedia processor based on metric computation

Media-processing applications, such as signal processing, 2D and 3D graphics rendering, and image compression, are the dominant workloads in many embedded systems today. The real-time constraints of those media applications have taxing demands on today's processor performances with low cost, low power and reduced design delay. To satisfy those challenges, a fast and efficient strategy consists in upgrading a low cost general purpose processor core. This approach is based on the personalization of a general RISC processor core according the target multimedia application requirements. Thus, if the extra cost is justified, the general purpose processor GPP core can be enforced with instruction level coprocessors, coarse grain dedicated hardware, ad hoc memories or new GPP cores. In this way the final design solution is tailored to the application requirements. The proposed approach is based on three main steps: the first one is the analysis of the targeted application using efficient metrics. The second step is the selection of the appropriate architecture template according to the first step results and recommendations. The third step is the architecture generation. This approach is experimented using various image and video algorithms showing its feasibility

Smart Chips for Smart Surroundings -- 4S

The overall mission of the 4S project (Smart Chips for Smart Surroundings) was to define and develop efficient flexible, reconfigurable core building blocks, including the supporting tools, for future Ambient System Devices. Reconfigurability offers the needed flexibility and adaptability, it provides the efficiency needed for these systems, it enables systems that can adapt to rapidly changing environmental conditions, it enables communication over heterogeneous wireless networks, and it reduces risks: reconfigurable systems can adapt to standards that may vary from place to place or standards that have changed during and after product development. In 4S we focused on heterogeneous building blocks such as analogue, hardwired functions, fine and coarse grain reconfigurable tiles and microprocessors. Such a platform can adapt to a wide application space without the need for specialized ASICs. A novel power aware design flow and runtime system was developed. The runtime system decides dynamically about the near-optimal application mapping to the given hardware platform. The overall concept was verified on hardware platforms based on an existing SoC and in a second step with novel silicon. DRM (Digital Radio Mondiale) and MPEG4 Video applications have been implemented on the platforms demonstrating the adaptability of the 4S concept

Cognitive Radio Programming: Existing Solutions and Open Issues

Software defined radio (sdr) technology has evolved rapidly and is now reaching market maturity, providing solutions for cognitive radio applications. Still, a lot of issues have yet to be studied. In this paper, we highlight the constraints imposed by recent radio protocols and we present current architectures and solutions for programming sdr. We also list the challenges to overcome in order to reach mastery of future cognitive radios systems.La radio logicielle a évolué rapidement pour atteindre la maturité nécessaire pour être mise sur le marché, offrant de nouvelles solutions pour les applications de radio cognitive. Cependant, beaucoup de problèmes restent à étudier. Dans ce papier, nous présentons les contraintes imposées par les nouveaux protocoles radios, les architectures matérielles existantes ainsi que les solutions pour les programmer. De plus, nous listons les difficultés à surmonter pour maitriser les futurs systèmes de radio cognitive

Performance and Energy Consumption Characterization and Modeling of Video Decoding on Multi-core Heterogenous SoC and their Applications

To meet the increasing complexity of mobile multimedia applications, the System on Chip (SoC) equipping modern mobile devices integrate powerful heterogeneous processing elements among which General Purpose Processors (GPP), Digital Signal Processors (DSP), hardware accelerator are the most common ones.Due to the ever-growing gap between battery lifetime and hardware/software complexity in addition to application computing power needs, the energy saving issue becomes crucial in the design of such systems. In this context, we propose a study aiming to enhance the understanding of the energy consumption behavior of video decoding on these kinds of systems. Accordingly, an end-to-end methodology for characterizing and modeling the performance and the energy consumption of video decoding on GPP and DSP is proposed. The characterization step is based on an exhaustive experimental methodology for evaluating, at different abstraction levels, the performance and the energy consumption of video decoding. It was achieved on embedded platforms on which were executed a wide range of video decoding configurations. This step highlighted the importance to consider different parameters which may pertain to different abstraction levels in evaluating the overall energy efficiency of a given system. The measurements obtained in this step were used to build empirically performance and energy models for video decoding on both GPP and DSP. The proposed models gave very accurate estimation (R 2 = 97%) of both the performance and the energy consumption of video decoding in terms of a rich set of parameters including the video quality and the processor frequency. Moreover, based on a multi-level characterization and sub-model decomposition approaches, we show how the developed models, unlike classic empirical models, are easily and rapidly generalizable to other platforms.Some possible applications using the developed models, in the context of adaptive video decoding, were proposed. In general, it consists to use the capability of the proposed performance model to predict the decoding time of a given video quality in dimensioning/scheduling the processing resources. Due to the increasing demand on High Definition (HD), the characterization methodology was extended to consider HD video decoding on both parallel multi-cores and hardware video accelerator. This part highlighted the potential of parallelism video decoding to increase the energy efficiency of video decoding and point out some open issues in this domain.Pour répondre à la complexité croissante des applications multimédia mobiles, les systèmes sur puce équipant les appareils mobiles modernes intègrent des unités de calcul puissantes et hétérogène. Parmi ces units de calcul, on peut trouver des processeurs à usage général, des processeur de traitement de signal et des accélérateurs matériels. En raison de l’écart toujours croissant entre la durée de vie des batteries et la demande de plus en plus importante en puissance de calcul, l’économie d’énergie devient un enjeu crucial dans la conception des systèmes mobiles. Cette problématique est accentuée par l’augmentation de la complexité des logiciels et architectures matériels utilisés. Dans ce contexte, nous proposons une étude visant à améliorer la compréhension des considérations énergétiques du décodage vidéo sur ce genre de systèmes. Nous proposerons ainsi une méthodologie pour la caractérisation et la modélisation des performances et de la consommation d’énergie du décodage vidéo, aussi bien sur des processeurs à usage général de type ARM que sur un processeurde traitement de signal. L’étape de caractérisation est basée sur une méthodologie expérimentale pour évaluer de façon exhaustive et à différents niveaux d’abstraction, les performances et la consommation d’énergie du décodage vidéo. Cette caractérisation a été réalisée sur des plates-formes embarquées sur lesquels ont été exécutés un large éventail de configurations du décodage vidéo. Cette étape a souligné l’importance d’examiner différents paramètres qui peuvent se rapporter à différents niveaux d’abstraction dans l’évaluation de l’efficacité énergétique globale d’un système donné. Les mesures obtenues dans cette étape ont été utilisées pour construire empiriquement des modèles de performance et de consommation d’énergie pour le décodage vidéo à la fois sur des processeurs à usage général type ARM et sur un processeur de traitement de signal. Les modèles proposés peuvent estimer avec une grande précision (R 2 = 97%) la performance et la consommation d’énergie de décodage vidéo en fonction d’un nombre de paramètres comprenant la qualité de la vidéo et la fréquence du processeur. En plus, en se basant sur une caractérisation multi-niveaux et une approches de modélisation par décomposition en sous-modèles, nous montrons comment les modèles développés, contrairement aux modèles empiriques classiques, sont facilement et rapidement généralisables à d’autres plates-formes. Nous proposerons également certaines applications possibles des modèles développés, dans le cadre du décodage vidéo adaptatif. En général, cela consiste à exploiter la capacité du modèle de performance proposé pour prédire le temps de décodage d’une qualité vidéo donnée afin de mieux dimensionner les ressources de calculs dans un but de réduire leur consommationd’énergie

Image Processing Using FPGAs

This book presents a selection of papers representing current research on using field programmable gate arrays (FPGAs) for realising image processing algorithms. These papers are reprints of papers selected for a Special Issue of the Journal of Imaging on image processing using FPGAs. A diverse range of topics is covered, including parallel soft processors, memory management, image filters, segmentation, clustering, image analysis, and image compression. Applications include traffic sign recognition for autonomous driving, cell detection for histopathology, and video compression. Collectively, they represent the current state-of-the-art on image processing using FPGAs

State of the art baseband DSP platforms for Software Defined Radio: A survey

Software Defined Radio (SDR) is an innovative approach which is becoming a more and more promising technology for future mobile handsets. Several proposals in the field of embedded systems have been introduced by different universities and industries to support SDR applications. This article presents an overview of current platforms and analyzes the related architectural choices, the current issues in SDR, as well as potential future trends.Peer reviewe