Efficient Smart CMOS Camera Based on FPGAs Oriented to Embedded Image Processing

This article describes an image processing system based on an intelligent ad-hoc camera, whose two principle elements are a high speed 1.2 megapixel Complementary Metal Oxide Semiconductor (CMOS) sensor and a Field Programmable Gate Array (FPGA). The latter is used to control the various sensor parameter configurations and, where desired, to receive and process the images captured by the CMOS sensor. The flexibility and versatility offered by the new FPGA families makes it possible to incorporate microprocessors into these reconfigurable devices, and these are normally used for highly sequential tasks unsuitable for parallelization in hardware. For the present study, we used a Xilinx XC4VFX12 FPGA, which contains an internal Power PC (PPC) microprocessor. In turn, this contains a standalone system which manages the FPGA image processing hardware and endows the system with multiple software options for processing the images captured by the CMOS sensor. The system also incorporates an Ethernet channel for sending processed and unprocessed images from the FPGA to a remote node. Consequently, it is possible to visualize and configure system operation and captured and/or processed images remotely

System-on-a-Chip (SoC) based Hardware Acceleration in Register Transfer Level (RTL) Design

Today, modern System-on-a-Chip (SoC) systems have grown rapidly due to the increased processing power, while maintaining the size of the hardware circuit. The number of transistors on a chip continues to increase, but current SoC designs may not be able to exploit the potential performance, especially with energy consumption and chip area becoming two major concerns. Traditional SoC designs usually separate software and hardware. Thus, the process of improving the system performance is a complicated task for both software and hardware designers. The aim of this research is to develop hardware acceleration workflow for software applications. Thus, system performance can be improved with constraints of energy consumption and on-chip resource costs. The characteristics of software applications can be identified by using profiling tools. Hardware acceleration can have significant performance improvement for highly mathematical calculations or repeated functions. The performance of SoC systems can then be improved, if the hardware acceleration method is used to accelerate the element that incurs performance overheads. The concepts mentioned in this study can be easily applied to a variety of sophisticated software applications. The contributions of SoC-based hardware acceleration in the hardware-software co-design platform include the following: (1) Software profiling methods are applied to H.264 Coder-Decoder (CODEC) core. The hotspot function of aimed application is identified by using critical attributes such as cycles per loop, loop rounds, etc. (2) Hardware acceleration method based on Field-Programmable Gate Array (FPGA) is used to resolve system bottlenecks and improve system performance. The identified hotspot function is then converted to a hardware accelerator and mapped onto the hardware platform. Two types of hardware acceleration methods – central bus design and co-processor design, are implemented for comparison in the proposed architecture. (3) System specifications, such as performance, energy consumption, and resource costs, are measured and analyzed. The trade-off of these three factors is compared and balanced. Different hardware accelerators are implemented and evaluated based on system requirements. 4) The system verification platform is designed based on Integrated Circuit (IC) workflow. Hardware optimization techniques are used for higher performance and less resource costs. Experimental results show that the proposed hardware acceleration workflow for software applications is an efficient technique. The system can reach 2.8X performance improvements and save 31.84% energy consumption by applying the Bus-IP design. The Co-processor design can have 7.9X performance and save 75.85% energy consumption

A Reconfigurable Processor for Heterogeneous Multi-Core Architectures

A reconfigurable processor is a general-purpose processor coupled with an FPGA-like reconfigurable fabric. By deploying application-specific accelerators, performance for a wide range of applications can be improved with such a system. In this work concepts are designed for the use of reconfigurable processors in multi-tasking scenarios and as part of multi-core systems

SPRING: A Sparsity-Aware Reduced-Precision Monolithic 3D CNN Accelerator Architecture for Training and Inference

CNNs outperform traditional machine learning algorithms across a wide range of applications. However, their computational complexity makes it necessary to design efficient hardware accelerators. Most CNN accelerators focus on exploring dataflow styles that exploit computational parallelism. However, potential performance speedup from sparsity has not been adequately addressed. The computation and memory footprint of CNNs can be significantly reduced if sparsity is exploited in network evaluations. To take advantage of sparsity, some accelerator designs explore sparsity encoding and evaluation on CNN accelerators. However, sparsity encoding is just performed on activation or weight and only in inference. It has been shown that activation and weight also have high sparsity levels during training. Hence, sparsity-aware computation should also be considered in training. To further improve performance and energy efficiency, some accelerators evaluate CNNs with limited precision. However, this is limited to the inference since reduced precision sacrifices network accuracy if used in training. In addition, CNN evaluation is usually memory-intensive, especially in training. In this paper, we propose SPRING, a SParsity-aware Reduced-precision Monolithic 3D CNN accelerator for trainING and inference. SPRING supports both CNN training and inference. It uses a binary mask scheme to encode sparsities in activation and weight. It uses the stochastic rounding algorithm to train CNNs with reduced precision without accuracy loss. To alleviate the memory bottleneck in CNN evaluation, especially in training, SPRING uses an efficient monolithic 3D NVM interface to increase memory bandwidth. Compared to GTX 1080 Ti, SPRING achieves 15.6X, 4.2X and 66.0X improvements in performance, power reduction, and energy efficiency, respectively, for CNN training, and 15.5X, 4.5X and 69.1X improvements for inference

Embedded electronic systems driven by run-time reconfigurable hardware

Abstract This doctoral thesis addresses the design of embedded electronic systems based on run-time reconfigurable hardware technology –available through SRAM-based FPGA/SoC devices– aimed at contributing to enhance the life quality of the human beings. This work does research on the conception of the system architecture and the reconfiguration engine that provides to the FPGA the capability of dynamic partial reconfiguration in order to synthesize, by means of hardware/software co-design, a given application partitioned in processing tasks which are multiplexed in time and space, optimizing thus its physical implementation –silicon area, processing time, complexity, flexibility, functional density, cost and power consumption– in comparison with other alternatives based on static hardware (MCU, DSP, GPU, ASSP, ASIC, etc.). The design flow of such technology is evaluated through the prototyping of several engineering applications (control systems, mathematical coprocessors, complex image processors, etc.), showing a high enough level of maturity for its exploitation in the industry.Resumen Esta tesis doctoral abarca el diseño de sistemas electrónicos embebidos basados en tecnología hardware dinámicamente reconfigurable –disponible a través de dispositivos lógicos programables SRAM FPGA/SoC– que contribuyan a la mejora de la calidad de vida de la sociedad. Se investiga la arquitectura del sistema y del motor de reconfiguración que proporcione a la FPGA la capacidad de reconfiguración dinámica parcial de sus recursos programables, con objeto de sintetizar, mediante codiseño hardware/software, una determinada aplicación particionada en tareas multiplexadas en tiempo y en espacio, optimizando así su implementación física –área de silicio, tiempo de procesado, complejidad, flexibilidad, densidad funcional, coste y potencia disipada– comparada con otras alternativas basadas en hardware estático (MCU, DSP, GPU, ASSP, ASIC, etc.). Se evalúa el flujo de diseño de dicha tecnología a través del prototipado de varias aplicaciones de ingeniería (sistemas de control, coprocesadores aritméticos, procesadores de imagen, etc.), evidenciando un nivel de madurez viable ya para su explotación en la industria.Resum Aquesta tesi doctoral està orientada al disseny de sistemes electrònics empotrats basats en tecnologia hardware dinàmicament reconfigurable –disponible mitjançant dispositius lògics programables SRAM FPGA/SoC– que contribueixin a la millora de la qualitat de vida de la societat. S’investiga l’arquitectura del sistema i del motor de reconfiguració que proporcioni a la FPGA la capacitat de reconfiguració dinàmica parcial dels seus recursos programables, amb l’objectiu de sintetitzar, mitjançant codisseny hardware/software, una determinada aplicació particionada en tasques multiplexades en temps i en espai, optimizant així la seva implementació física –àrea de silici, temps de processat, complexitat, flexibilitat, densitat funcional, cost i potència dissipada– comparada amb altres alternatives basades en hardware estàtic (MCU, DSP, GPU, ASSP, ASIC, etc.). S’evalúa el fluxe de disseny d’aquesta tecnologia a través del prototipat de varies aplicacions d’enginyeria (sistemes de control, coprocessadors aritmètics, processadors d’imatge, etc.), demostrant un nivell de maduresa viable ja per a la seva explotació a la indústria

Metoda projektovanja namenskih programabilnih hardverskih akceleratora

Namenski računarski sistemi se najčesće projektuju tako da mogu da podrže izvršavanje većeg broja željenih aplikacija. Za postizanje što veće efikasnosti, preporučuje se korišćenje specijalizovanih procesora Application Specific Instruction Set Processors–ASIPs, na kojima se izvršavanje programskih instrukcija obavlja u za to projektovanim i nezavisnimhardverskim blokovima (akceleratorima). Glavni razlog za postojanje nezavisnih akceleratora jeste postizanjemaksimalnog ubrzanja izvršavanja instrukcija. Me ¯ dutim, ovakav pristup podrazumeva da je za svaki od blokova potrebno projektovati integrisano (ASIC) kolo, čime se bitno povećava ukupna površina procesora. Metod za smanjenje ukupne površine jeste primena DatapathMerging tehnike na dijagrame toka podataka ulaznih aplikacija. Kao rezultat, dobija se jedan programabilni hardverski akcelerator, sa mogućnosću izvršavanja svih željenih instrukcija. Međutim, ovo ima negativne posledice na efikasnost sistema. često se zanemaruje činjenica da, usled veoma ograničene fleksibilnosti ASIC hardverskih akceleratora, specijalizovani procesori imaju i drugih nedostataka. Naime, u slučaju izmena, ili prosto nadogradnje, specifikacije procesora u završnimfazama projektovanja, neizbežna su velika kašnjenja i dodatni troškovi promene dizajna. U ovoj tezi je pokazano da zahtevi za fleksibilnošću i efikasnošću ne moraju biti međusobno isključivi. Demonstrirano je je da je moguce uneti ograničeni nivo fleksibilnosti hardvera tokom dizajn procesa, tako da dobijeni hardverski akcelerator može da izvršava ne samo aplikacije definisane na samom početku projektovanja, već i druge aplikacije, pod uslovom da one pripadaju istom domenu. Drugim rečima, u tezi je prezentovana metoda projektovanja fleksibilnih namenskih hardverskih akceleratora. Eksperimentalnom evaluacijom pokazano je da su tako dobijeni akceleratori u većini slučajeva samo do 2 x veće površine ili 2 x većeg kašnjenja od akceleratora dobijenih primenom DatapathMerging metode, koja pritom ne pruža ni malo dodatne fleksibilnosti.Typically, embedded systems are designed to support a limited set of target applications. To efficiently execute those applications, they may employ Application Specific Instruction Set Processors (ASIPs) enriched with carefully designed Instructions Set Extension (ISEs) implemented in dedicated hardware blocks. The primary goal when designing ISEs is efficiency, i.e. the highest possible speedup, which implies synthesizing all critical computational kernels of the application dataflow graphs as an Application Specific Integrated Circuit (ASICs). Yet, this can lead to high on-chip area dedicated solely to ISEs. One existing approach to decrease this area by paying a reasonable price of decreased efficiency is to perform datapath merging on input dataflow graphs (DFGs) prior to generating the ASIC. It is often neglected that even higher costs can be accidentally incurred due to the lack of flexibility of such ISEs. Namely, if late design changes or specification upgrades happen, significant time-to-market delays and nonrecurrent costs for redesigning the ISEs and the corresponding ASIPs become inevitable. This thesis shows that flexibility and efficiency are not mutually exclusive. It demonstrates that it is possible to introduce a limited amount of hardware flexibility during the design process, such that the resulting datapath is in fact reconfigurable and thus can execute not only the applications known at design time, but also other applications belonging to the same application-domain. In other words, it proposes a methodology for designing domain-specific reconfigurable arrays out of a limited set of input applications. The experimental results show that resulting arrays are usually around 2£ larger and 2£ slower than ISEs synthesized using datapath merging, which have practically null flexibility beyond the design set of DFGs

Myths and Legends in High-Performance Computing

In this thought-provoking article, we discuss certain myths and legends that are folklore among members of the high-performance computing community. We gathered these myths from conversations at conferences and meetings, product advertisements, papers, and other communications such as tweets, blogs, and news articles within and beyond our community. We believe they represent the zeitgeist of the current era of massive change, driven by the end of many scaling laws such as Dennard scaling and Moore's law. While some laws end, new directions are emerging, such as algorithmic scaling or novel architecture research. Nevertheless, these myths are rarely based on scientific facts, but rather on some evidence or argumentation. In fact, we believe that this is the very reason for the existence of many myths and why they cannot be answered clearly. While it feels like there should be clear answers for each, some may remain endless philosophical debates, such as whether Beethoven was better than Mozart. We would like to see our collection of myths as a discussion of possible new directions for research and industry investment