Floorplanning problem has been extensively explored for homogeneous FPGAs. Most modern FPGAs consist of heterogeneous resources in the form of configurable logic blocks, DSP blocks, BRAMs and more. Very little work has been done for heterogeneous FPGAs. In addition, features like partial reconfigurability allow on-the-fly changes to the executable design that can result in enhanced performance and very efficient utilization of resources. In this paper, we have designed a floorplanner for Partially Reconfigurable (PR) designs in FPGA that smartly decides one of the three proposed resource allocation schemes to floorplan a particular type of reconfigurable region. We also propose a White Space Detection algorithm for efficient management of white space inside an FPGA in order to reduce the area and the wire length. The floorplanner is demonstrated on Xilinx Virtex 5 and Artix 7 FPGA architectures and can be easily integrated with existing vendor-supplied Place and Route tools. The main objective of the floorplanner is to reduce the wire length, minimize wasted resources and the area. The performance of our floorplanner is evaluated using MCNC benchmarks. We have compared our proposed floorplanner with other previously published results reported in the literature. We observe a substantial improvement in the overall wire length as well as the execution time. Also, the floorplanner was integrated with vendor supplied place and route tools (Xilinx Vivado) to automate the floorplanning flow. The automation process was tested on a partially reconfigurable median filter used in image processing applications.
Partial Reconfiguration (PR) is a technique that allows reconfiguring the FPGA chip at runtime. However, current design support tools require manual floorplanning of the partial modules. Several approaches have been proposed in this field, but only a few of them consider all aspects of PR, like the shape and the aspect ratio of the reconfigurable region. Most of them are defined for old FPGA architectures and have a high computational time. This paper introduces an efficient automatic floorplanning algorithm, which takes into account the heterogeneous architectures of modern FPGA families, as …
Existing scrubbing techniques for SEU mitigation on FPGAs do not guarantee an error-free operation after SEU recovering if the affected configuration bits do belong to feedback loops of the implemented circuits. In this paper, we a) provide a netlist-based circuit analysis technique to distinguish so-called critical configuration bits from essential bits in order to identify configuration bits which will need also state-restoring actions after a recovered SEU and which not. Furthermore, b) an alternative classification approach using fault injection is developed in order to compare both classi…
Confronted with the challenge of high performance for applications and the restriction of hardware resources for field-programmable gate arrays (FPGAs), partial dynamic reconfiguration (PDR) technology is anticipated to accelerate the reconfiguration process and alleviate the device shortage. In this paper, we propose an integrated optimization framework for task partitioning, scheduling and floorplanning on partially dynamically reconfigurable FPGAs. The partitions, schedule, and floorplan of the tasks are represented by the partitioned sequence triple P-ST (PS,QS,RS), where (PS,QS) is a hybr…
Multi-die FPGAs are widely adopted to deploy large hardware accelerators. Two factors impede the performance optimization of HLS designs implemented on multi-die FPGAs. On the one hand, the long net delay due to nets crossing die-boundaries results in an NP-hard problem to properly floorplan and pipeline an application. On the other hand, traditional automated searching flow for HLS directive optimizations targets single-die FPGAs, and hence, it cannot consider the resource constraints on each die and the timing issue incurred by the die-crossings. Further, it leads to an excessively long runt…
Field Programmable Gate Array technology (FPGA) is a highly configurable option for implementing many sophisticated signal processing tasks in Software Defined Radios (SDRs). Those types of radios are realized using highly configurable hardware platforms. Convolutional codes are used in every robust digital communication system and Viterbi algorithm is employed in wireless communications to decode the convolutional codes. Such decoders are complex and dissipate large amount of power. In this paper, a low power-reconfigurable Viterbi decoder for WiMAX receiver is described using a VHDL code for…
Simultaneous Localization and Mapping (SLAM) estimates agents' trajectories and constructs maps, and localization is a fundamental kernel in autonomous machines at all computing scales, from drones, AR, VR to self-driving cars. In this work, we present an energy-efficient and runtime-reconfigurable FPGA-based accelerator for robotic localization. We exploit SLAM-specific data locality, sparsity, reuse, and parallelism, and achieve >5x performance improvement over the state-of-the-art. Especially, our design is reconfigurable at runtime according to the environment to save power while sustainin…
Adaptive systems based on field programmable gate array (FPGA) architectures can greatly benefi t fro m th e high degree of flexibility offered by dynamic partial reconfiguration (DPR). By using this technique, hardware tasks can be loaded and reloaded on demand depending on the system requirements. In this paper, we propose to use the DPR for dynamic and adaptive implementation of a video cut detection application based on the MPEG-7 color structure descriptor (CSD). In the proposed implementation, different scenarios have been tested. Depending on the application and the system requirements,…
With the emerging big data applications of Machine Learning, Speech Recognition, Artificial Intelligence, and DNA Sequencing in recent years, computer architecture research communities are facing the explosive scale of various data explosion. To achieve high efficiency of data-intensive computing, studies of heterogeneous accelerators which focus on latest applications, have become a hot issue in computer architecture domain. At present, the implementation of heterogeneous accelerators mainly relies on heterogeneous computing units such as Application-specific Integrated Circuit (ASIC), Graphi…
In this treatise, my research on methods to improve efficiency, reliability, and security of reconfigurable hardware systems, i.e., FPGAs, through partial dynamic reconfiguration is outlined. The efficiency of reconfigurable systems can be improved by loading optimized data paths on-the-fly on an FPGA fabric. This technique was applied to the acceleration of SQL queries for large database applications as well as for image and signal processing applications. The focus was not only on performance improvements and resource efficiency, but also the energy efficiency has been significantly improved…
The exponential emergence of Field Programmable Gate Array (FPGA) has accelerated the research of hardware implementation of Deep Neural Network (DNN). Among all DNN processors, domain specific architectures, such as, Google's Tensor Processor Unit (TPU) have outperformed conventional GPUs. However, implementation of TPUs in reconfigurable hardware should emphasize energy savings to serve the green computing requirement. Voltage scaling, a popular approach towards energy savings, can be a bit critical in FPGA as it may cause timing failure if not done in an appropriate way. In this work, we pr…
The well known method C-Slow Retiming (CSR) can be used to automatically convert a given CPU into a multithreaded CPU with independent threads. These CPUs are then called streaming or barrel processors. System Hyper Pipelining (SHP) adds a new flexibility on top of CSR by allowing a dynamic number of threads to be executed and by enabling the threads to be stalled, bypassed and reordered. SHP is now applied on the programming elements (PE) of a coarse-grained reconfigurable architecture (CGRA). By using SHP, more performance can be achieved per PE. Fork-Join operations can be implemented on a …
In todays world, high-power computing applications such as image processing, digital signal processing, graphics, and robotics require enormous computing power. These applications use matrix operations, especially matrix multiplication. Multiplication operations require a lot of computational time and are also complex in design. We can use field-programmable gate arrays as low-cost hardware accelerators along with a low-cost general-purpose processor instead of a high-cost application-specific processor for such applications. In this work, we employ an efficient Strassens algorithm for matrix …
Scalability of Field Programmable Gate Array (FPGA) using spin MOSFET (spin FPGA) with magnetocurrent (MC) ratio in the range of 100% to 1000% is discussed for the first time. Area and speed of million-gate spin FPGA are numerically benchmarked with CMOS FPGA for 22nm, 32nm and 45nm technologies including 20% transistor size variation. We show that area is reduced and speed is increased in spin FPGA owing to the nonvolatile memory function of spin MOSFET.
A Reconfigurable Framework for AI-FPGA Agent Integration and Acceleration
Purdue University（普渡大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；University of Moratuwa（莫拉图瓦大学）；Stanford University（斯坦福大学）；Istinye University（伊斯提耶大学）
High Performance Computing (HPC) platforms allow scientists to model computationally intensive algorithms. HPC clusters increasingly use General-Purpose Graphics Processing Units (GPGPUs) as accelerators; FPGAs provide an attractive alternative to GPGPUs for use as co-processors, but they are still far from being mainstream due to a number of challenges faced when using FPGA-based platforms. Our research aims to make FPGA-based high performance computing more accessible to the scientific community. In this work we present the results of investigating the acceleration of a particular atmospheri…
Field Programmable Gate Arrays (FPGAs) are more prone to be affected by transient faults in presence of radiation and other environmental hazards compared to Application Specific Integrated Circuits (ASICs). Hence, error mitigation and recovery techniques are absolutely necessary to protect the FPGA hardware from soft errors arising due to such transient faults. In this paper, a new efficient multi-bit error correcting method for FPGAs is proposed using adaptive cross parity check (ACPC) code. ACPC is easy to implement and the needed decoding circuit is also simple. In the proposed scheme tota…
Development of modern integrated circuit technologies makes it feasible to develop cheaper, faster and smaller special purpose signal processing function circuits. Digital Signal processing functions are generally implemented either on ASICs with inflexibility, or on FPGAs with bottlenecks of relatively smaller utilization factor or lower speed compared to ASIC. Field Programmable DSP Array (FPDA) is the proposed DSP dedicated device, redolent to FPGA, but with basic fixed common modules (CMs) (like adders, subtractors, multipliers, scaling units, shifters) instead of CLBs. This paper introduc…
With the continued growth in field-programmable gate array (FPGA) capacity and their incorporation into new environments such as datacenters, we have witnessed the introduction of a new class of reconfigurable acceleration devices (RADs) that go beyond conventional FPGA architectures. These devices combine a reconfigurable fabric with coarse-grained domain-specialized accelerator blocks all connected via a high-performance packet-switched network-on-chip (NoC) for efficient system-wide communication. However, we lack the tools necessary to efficiently explore the huge design space for RADs, st…
Matrix-matrix multiplication is a key computational kernel for numerous applications in science and engineering, with ample parallelism and data locality that lends itself well to high-performance implementations. Many matrix multiplication-dependent applications can use reduced-precision integer or fixed-point representations to increase their performance and energy efficiency while still offering adequate quality of results. However, precision requirements may vary between different application phases or depend on input data, rendering constant-precision solutions ineffective. BISMO, a vecto…
Every year, the computing resources available on dynamically partially reconfigurable devices increase enormously. In the near future, we expect many applications to run on a single reconfigurable device. In this paper, we present a concept for multitasking on dynamically partially reconfigurable systems called virtual area management. We explain its advantages, show its challenges, and discuss possible solutions. Furthermore, we investigate one problem in more detail: Packing modules with time-varying resource requests. This problem from the reconfigurable computing field results in a complet…
Autoencoders are unsupervised neural networks that are used to process and compress input data and then reconstruct the data back to the original data size. This allows autoencoders to be used for different processing applications such as data compression, image classification, image noise reduction, and image coloring. Hardware-wise, re-configurable architectures like Field Programmable Gate Arrays (FPGAs) have been used for accelerating computations from several domains because of their unique combination of flexibility, performance, and power efficiency. In this paper, we look at the differ…
Graduate School of Information, Production and Systems, Waseda University（早稻田大学信息生产系统研究生院）
Large-scale deep convolutional neural networks (CNNs) are widely used in machine learning applications. While CNNs involve huge complexity, VLSI (ASIC and FPGA) chips that deliver high-density integration of computational resources are regarded as a promising platform for CNN's implementation. At massive parallelism of computational units, however, the external memory bandwidth, which is constrained by the pin count of the VLSI chip, becomes the system bottleneck. Moreover, VLSI solutions are usually regarded as a lack of the flexibility to be reconfigured for the various parameters of CNNs. T…
In this paper, the acceleration of algorithms using a design of a field programmable gate array (FPGA) as a prototype of a static dataflow architecture is discussed. The static dataflow architecture using operators interconnected by parallel buses was implemented. Accelerating algorithms using a dataflow graph in a reconfigurable system shows the potential for high computation rates. The results of benchmarks implemented using the static dataflow architecture are reported at the end of this paper.
Trusted Execution Environments (TEEs) are used to protect sensitive data and run secure execution for security-critical applications, by providing an environment isolated from the rest of the system. However, over the last few years, TEEs have been proven weak, as either TEEs built upon security-oriented hardware extensions (e.g., Arm TrustZone) or resorting to dedicated secure elements were exploited multiple times. In this project, we introduce Trusted Execution Environments On-Demand (TEEOD), a novel TEE design that leverages the programmable logic (PL) in the heterogeneous system on chips …
In data communication via internet, security is becoming one of the most influential aspects. One way to support it is by classifying and filtering ethernet packets within network devices. Packet classification is a fundamental task for network devices such as routers, firewalls, and intrusion detection systems. In this paper we present architecture of fast and reconfigurable Packet Classification Engine (PCE). This engine is used in FPGA-based firewall. Our PCE inspects multi-dimensional field of packet header sequentially based on tree-based algorithm. This algorithm simplifies overall syste…
In next generation wireless communication system, wireless transceivers should be able to handle wideband input signals compromising of multiple communication standards.Such multi-standard wireless communication receivers (MWCRs) need filter bank to extract the desired signal of interest from wideband input spectrum and bring it to the baseband for further signal processing tasks such as spectrum sensing, modulation classification,demodulation etc.In MWCRs,rather any wireless receivers, modulated filter banks, such as Discrete Fourier Transform Filter Banks (DFTFB), are preferred due to their …
University of Southern California（南加利福尼亚大学）；US Army Research Lab（美国陆军研究实验室）
Tensor decomposition has become an essential tool in many applications in various domains, including machine learning. Sparse Matricized Tensor Times Khatri-Rao Product (MTTKRP) is one of the most computationally expensive kernels in tensor computations. Despite having significant computational parallelism, MTTKRP is a challenging kernel to optimize due to its irregular memory access characteristics. This paper focuses on a multi-faceted memory system, which explores the spatial and temporal locality of the data structures of MTTKRP. Further, users can reconfigure our design depending on the b…
In this chapter, we study the feasibility of implementing SOR in reconfigurable hardware. We use Handel-C, a higher level design tool, to code our design, which is analyzed, synthesized, and placed and routed using the FPGAs proprietary software (DK Design Suite, Xilinx ISE 8.1i, and Quartus II 5.1). We target Virtex II Pro, Altera Stratix, and Spartan3L, which is embedded in the RC10 FPGA-based system from Celoxica. We report our timing results when targeting Virtex II Pro and compare them to software version results written in C++ and running on a general purpose processor (GPP).
FPGAs are going mainstream. Major companies that were not traditionally FPGA-focused are now seeking ways to exploit the benefits of reconfigurable technology and provide it to their customers. In order to do so, a debug ecosystem that provides for effective visibility into a working design and quick debug turn-around times is essential. Overlays have the opportunity to play a key role in this ecosystem. In this overview paper, we discuss how an overlay fabric that allows the user to rapidly add debug instrumentation to a design can be created and exploited. We discuss the requirements of such…
In the context of embedded systems design, two important challenges are still under investigation. First, improve real-time data processing, reconfigurability, scalability, and self-adjusting capabilities of hardware components. Second, reduce power consumption through low-power design techniques as clock gating, logic gating, and dynamic partial reconfiguration (DPR) capabilities. Today, several application, e.g., cryptography, Software-defined radio or aerospace missions exploit the benefits of DPR of programmable logic devices. The DPR allows well defined reconfigurable FPGA region to be mo…
The use of reconfigurable computing, and FPGAs in particular, has strong potential in the field of High Performance Computing (HPC). However the traditionally high barrier to entry when it comes to programming this technology has, until now, precluded widespread adoption. To popularise reconfigurable computing with communities such as HPC, Xilinx have recently released the first version of Vitis, a platform aimed at making the programming of FPGAs much more a question of software development rather than hardware design. However a key question is how well this technology fulfils the aim, and wh…
High-Level Synthesis has introduced reconfigurable logic to a new world -- that of software development. The newest wave of HLS tools has been successful, and the future looks bright. But is HLS the end-all-be-all to FPGA acceleration? Is it enough to allow non-experts to program FPGAs successfully, even when dealing with troublesome data structures and complex control flows -- such as those often encountered in graph algorithms? We take a look at the panorama of adoption of HLS by the software community, focusing on graph analysis in particular in order to generalise to \textit{FPGA-unfriendl…
Modern embedded and cyber-physical systems require every day more performance, power efficiency and flexibility, to execute several profiles and functionalities targeting the ever growing adaptivity needs and preserving execution efficiency. Such requirements pushed designers towards the adoption of heterogeneous and reconfigurable substrates, which development and management is not that straightforward. Despite acceleration and flexibility are desirable in many domains, the barrier of hardware deployment and operation is still there since specific advanced expertise and skills are needed. Rel…
Reconfigurable Computing Challenge: Real-Time Graph Neural Networks for Online Event Selection in Big Science
Institute for Information Processing Technology, Karlsruhe Institute of Technology, Germany（信息处理技术研究所，卡尔斯鲁厄理工学院，德国）；Institute of Experimental Particle Physics, Karlsruhe Institute of Technology, Germany（实验粒子物理学研究所，卡尔斯鲁厄理工学院，德国）
本文提出了一种实时部署动态图神经网络的端到端演示器，用于 Belle II 电磁量能器硬件触发，利用 FPGA fabric 和 AI Engine tiles 实现 290 万事件每秒的吞吐量，减少资源消耗并提升性能。
This paper proposes an efficient reconfigurable hardware design for speech enhancement based on multi band spectral subtraction algorithm and involving both magnitude and phase components. Our proposed design is novel as it estimates environmental noise from speech adaptively utilizing both magnitude and phase components of the speech spectrum. We performed multi-band spectrum subtraction by dividing the noisy speech spectrum into different non-uniform frequency bands having varying signal to noise ratio (SNR) and subtracting the estimated noise from each of these frequency bands. This results…
Convolutional Neural Networks (CNNs) are widely used in deep learning applications, e.g. visual systems, robotics etc. However, existing software solutions are not efficient. Therefore, many hardware accelerators have been proposed optimizing performance, power and resource utilization of the implementation. Amongst existing solutions, Field Programmable Gate Array (FPGA) based architecture provides better cost-energy-performance trade-offs as well as scalability and minimizing development time. In this paper, we present a model-independent reconfigurable co-processing architecture to accelera…
