MaNaBIT: A Versatile Tool for Manipulating and Analyzing FPGA Bitstreams

Recent research has shown that field programmable gate arrays (FPGAs) have a large potential for accelerating demanding applications, such as high performance digital signal process applications with low-volume market. The loss of generality in the architecture is one disadvantage of using FPGAs, however, the reconfigurability of FPGAs allow reprogramming for other applications. Therefore, a uniform FPGA-based architecture, an efficient programming model, and a simple mapping method are paramount for the wide acceptance of FPGA technology. This paper presents MASALA, a dynamically reconfigurable FPGA-based accelerator for parallel programs written in thread-intensive and explicit memory management (TEMM) programming models. Our system uses a TEMM programming model to parallelize demanding applications, including application decomposition into separate thread blocks and compute and data load/store decoupling. Hardware engines are included into MASALA using partial dynamic reconfiguration modules, each of which encapsulates a thread process engine that implements the hardware's thread functionality. A data dispatching scheme is also included in MASALA to enable the explicit communication of multiple memory hierarchies such as interhardware engines, host processors, and hardware engines. Finally, this paper illustrates a multi-FPGA prototype system of the presented architecture: MASALA-SX. A large synthetic aperture radar image formatting experiment shows that MASALA's architecture facilitates the construction of a TEMM program accelerator by providing greater performance and less power consumption than current CPU platforms, without sacrificing programmability, flexibility, and scalability.

Field Programmable Gate Array (FPGA) architectures, such as Xilinx's Virtex-4 up to 7 series, have enabled partial and dynamic run-time self-reconfiguration for a long time. This feature enables the substitution of parts of a hardware design implemented on this re-configurable hardware, and therefore makes it possible for a system to adapt to the actual demands of applications. Dynamic Partial Reconfiguration (DPR) is an interesting technique that permits to share a part of the FPGA between different dedicated functions or hardware accelerators. Many domains may benefit from this technique including the Internet of Things (IoT), automotive industry, etc. However, many parameters, such as reconfiguration overhead, idle power consumption and performance trade-off, must be considered. In this paper, we provide a precise estimation of the power consumption when the DPR process is running in order to evaluate its influence on the performance of the global design. For this purpose, a Software/Hardware (SW/HW) system was implemented and the results were extracted in real-time using Zynq System on Chip SoC devices.

Debugging of Field-Programmable Gate Arrays (FPGAs) is a difficult task due to the limited access to the internal signals of the design. Embedded logic analyzers enhance the signal observability for FPGAs. These analyzers are implemented on the FPGA resources and they use the embedded memory blocks as trace buffers, so a limited number of signals can be observed using these analyzers due to resources constraints. Changing the traced set of signals requires re-synthesis, placement and routing of the whole design. In this paper, we propose a new methodology for FPGA debugging to change dynamically the set of signals to be observed at runtime, and consequently minimize the time required for debugging. The proposed methodology utilizes the Dynamic Partial Reconfiguration (DPR) technique to dynamically switch between different sets of signals. DPR creates a reconfigurable module (RM) to route each set of signals to an embedded logic analyzer. We demonstrate the proposed approach using Xilinx FPGA tools, finding that changing the set of signals to be observed requires only few milli-seconds to re-program the reconfigurable region (RR). The area overhead of the proposed methodology is lower than other traditional methods of using multiplexers as the DPR allows the routing module to only use buffers to connect a set of signals to the embedded logic analyzer.

This paper presents a novel cell architecture for evolvable systolic arrays. HexCell is a tile-able processing element with a hexagonal shape that can be implemented and dynamically reconfigured on field-programmable gate arrays (FPGAs). The cell contains a functional unit, three input ports, and three output ports. It supports two concurrent configuration schemes: dynamic partial reconfiguration (DPR), where the functional unit is partially reconfigured at run time, and virtual reconfiguration circuit (VRC), where the cell output port bypasses one of the input data or selects the functional unit output. Hence, HexCell combines the merits of DPR and VRC including resource-awareness, reconfiguration speed and routing flexibility. In addition, the cell structure supports pipelining and data synchronization for achieving high throughput for data-intensive applications like image processing. A HexCell is represented by a binary string (chromosome) that encodes the cell's function and the output selections. Our developed evolvable HexCell array supports more inputs and outputs, a variety of possible datapaths, and has faster reconfiguration, compared to the state-of-the-art systolic array while maintaining the same resource utilization. Moreover, by using the same genetic algorithm on the two systolic arrays, results show that the HexCell array has higher throughput and can evolve faster than state-of-the-art array.

Application specific integrated circuits are used to provide real-time performance of handheld devices within the compact physical size and low energy dissipation. However, these circuits are inflexible for any modification like redesign and refabrication. Reconfigurable architectures are promising alternative for conventional computing devices such as application-specific integrated circuits. It provides high-level flexibility to adapt the applications through dynamic hardware customization even after fabrication. A reconfigurable computing system design considers the peculiarities of the targeted application domain along with architectural issues like dynamic reconfiguration, partial reconfiguration, hardware/software co-design and context management. A brief survey has been carried out to understand the concepts and applications of CGRA, FPGA, SoC, NoC, MPSoC, dynamic partial reconfiguration, hardware/software co-design, design-space exploration, multi-objective optimization, genetic algorithm and reconfigurable computing on wireless communications. This paper deals with the survey on modeling, design, implementation and evaluation of coarse-grained dynamically reconfigurable processor architectures targeting the real-time embedded computing domain.

There is still no partial reconfiguration tool support on low-cost Field Programmable Gate Arrays (FPGAs) such as old-fashioned Spartan-3 and state-of-the-art Spartan-6 FPGA families by Xilinx. This forces the designers and engineers, who are using the partial reconfiguration capability of FPGAs, to use expensive families such as Virtex-4, Virtex-5 and Virtex-6 which are officially supported by partial reconfiguration (PR) software. Moreover, Xilinx still does not offer a portable, dedicated self-reconfiguration engine for all of the FPGAs. Self-reconfiguration is achieved with general-purpose processors such as MicroBlaze and PowerPC which are too overqualified for this purpose. In this study, we propose a new self-reconfiguration mechanism for Spartan-6 FPGAs. This mechanism can be used to implement large and complex designs on small FPGAs as chip area can be dramatically reduced by exploiting the dynamic partial reconfiguration feature for on-demand functionality loading and maximal utilization of the hardware. This approach is highly attractive for designing low-cost compute-intensive applications such as high performance image processing systems. For Spartan-6 FPGAs, we have developed hard-macros and exploited the self-reconfiguration engine, compressed Parallel Configuration Access Port (cPCAP) [1], that we designed for Spartan-3. The modified cPCAP core with block RAM controller, bitstream decompressor unit and Internal Configuration Access Port (ICAP) Finite State Machine (FSM) occupies only about 82 of 6,822 slices (1.2% of whole device) on a Spartan-XC6SLX45 FPGA and it achieves the maximum theoretical reconfiguration speed of 200MB/s (ICAP, 16-bit at 100MHz) proposed by Xilinx. We have also implemented a Reconfigurable Processing Element (RPE) whose arithmetic unit can be reconfigured on-the-fly. Multiple RPEs can be utilized to design a General Purpose Image Processing System (GPIPS) that can implement a number of different algorithms during runtime. As an illustrative example, we programmed the GPIPS on Spartan-6 for switching between two applications on-demand such as two-dimensional filtering and block-matching.

K-nearest neighbour (KNN) is a supervised classification technique that is widely used in many fields of study to classify unknown queries based on some known information about the dataset. KNN is known to be robust and simple to implement when dealing with data of small size. However it performs slowly when data are large and have high dimensions. Therefore, KNN classifiers can benefit from the parallelism offered by Field Programmable Gate Arrays (FPGAs) to accelerate the algorithm. In addition, the KNN classifier is sensitive to the user defined parameter (K) which is the number of nearest neighbours. This parameter is known to affect the performance of the classifier; thus users would want the classifier to be easily adaptable to different values of K. In this work, we propose two adaptive FPGA architectures of the KNN classifier, and compare the implementations of each one of them with an equivalent implementation running on a general purpose processor (GPP). The proposed hardware implementations outperformed GPP by 76× for the first architecture and 68× for the second. In addition, we propose a novel dynamic partial reconfiguration (DPR) architecture of the KNN classifier, which allows for an efficient dynamic partial reconfiguration of a classifier implementation on FPGA for different K's. This DPR implementation offers 5× speed-up in the reconfiguration time of a KNN classifier on FPGA.

Dynamic Partial Reconfiguration (DPR) of Field Programmable Gate Array (FPGA) was introduced to overcome the need for more resources on the FPGA.PDR could change the functionality and efficiency of the system in order to accommodate more hardware module, save power and fabric area.PDR involves the design of modules that are independent of each other so that they can be loaded in the same fabric area one after other. In this paper the authors reconfigure some specific region of the FPGA with a new functionality at run time while the remaining areas remain static during this time. The complexities during the runtime can be simplified by a tool called PLANAHEAD which was introduced by XILINX that is able to implement during runtime reconfigurable systems for all VIRTEX field programmable gate array. PLANAHEAD is the first graphical environment for partial reconfiguration which gives the flexibility for reducing the board space, change a design in the field and also reduces the power consumption.

Today’s mobile networks are moving towards creating base stations and mobile stations that are compatible with many standards simultaneously. One way of achieving it is to reconfigure and time multiplex the processing resources based on the present necessity. Field Programmable Gate Arrays have become one of the best choices for implementing digital signal processing and Software Defined Radio platforms due to advancements in VLSI over the past few decades. Partial Reconfiguration has regained its importance in the last decade and is the one of the best methodologies to implement an Software Defined Radio. This paper presents an implementation of physical layer specifications of IEEE 802.11g using dynamic partial reconfiguration on FPGA. The Orthogonal Frequency Division Multiplexing (OFDM) Physical layer is implemented with various encoding and modulation schemes to achieve different data rates. The design has been implemented in Xilinx Virtex-5 board. Keywords: FPGA, IEEE 802.11g, OFDM, Partial Reconfiguration

Real-time performance is the primary requirement for edge computing systems. However, with the surge in data volume and the growing demand for computing power, a computing framework consisting solely of CPUs is no longer competent. As a result, CPU+ heterogeneous architecture using accelerators to improve edge computing systems' computing capacity has received great attention. The type of accelerators determines the performance of the edge computing system largely. The accelerators include Graphics Processing Unit (GPU), Application Specific Integrated Circuit (ASIC) and Field Programmable Gate Array (FPGA). FPGAs with its reconfigurability and high energy efficiency are widely used in many edge computing scenarios. Nontheless, the performance depends also on the scheduling efficiency between software tasks on CPUs and hardware tasks on FPGAs. Unfortunately, the existing strategies have not fully exploited the differences between hardware and software tasks, thus resulting in low scheduling efficiency. This paper proposes a task scheduling framework on the Dynamic Partial Reconfiguration (DPR) platform. We take full account of the characteristics of task switching overhead and predictable execution time of hardware tasks in DPR, and reduce the number of task-switching times and active tasks in the system, thus improving the scheduling efficiency. We conduct a set of experiments on the Zynq platform to verify the proposed framework. Experimental results demonstrate that when the execution time of the accelerator exceeds the reconfiguration cost by an order of magnitude, the efficiencies of all the cases are more than 98%, and the efficiencies can reach 90%-98% in the same order of magnitude.

International audience

Field programmable gate array (FPGA) is ubiquitous nowadays and is applied to many areas. Dynamic partial reconfiguration (DPR) is introduced to most modern FPGAs, enabling changing the function of a part of the FPGA by dynamically loading new bitstreams to the logic regions without affecting the function of other parts of the FPGA. However, delivering the powerful capacity of the DPR FPGA to the user depends on the efficient partitioning and scheduling technology. This article proposes the module merging technique for the partitioning and scheduling problem to reduce the reconfiguration overhead and improve the schedule performance. An exact approach based on the integer linear programming (ILP) for the partitioning and scheduling problem with module merging is proposed. The ILP-based approach is capable of solving the problem optimally, and can be used to further improve the performance of schedules produced by other non-optimal algorithms; however, it is time-consuming to solve large-scale problems. Therefore, a K-sliced-ILP algorithm based on the methodology of divide-and-conquer is proposed, which is able to reduce the time complexity significantly with the solution quality being degraded marginally. Experiments are carried out with a set of real-life applications, and the result demonstrates the effectiveness of the proposed methods.

The soft computing algorithms are being nowadays used for various multi input multi output complicated non linear control applications. This paper presented the development and implementation of back propagation of multilayer perceptron architecture developed in FPGA using VHDL. The usage of the FPGA (Field Programmable Gate Array) for neural network implementation provides flexibility in programmable systems. For the neural network based instrument prototype in real time application. The conventional specific VLSI neural chip design suffers the limitation in time and cost. With low precision artificial neural network design, FPGA have higher speed and smaller size for real time application than the VLSI design. The challenges are finding an architecture that minimizes the hardware cost, maximizing the performance, accuracy. The goal of this work is to realize the hardware implementation of neural network using FPGA. Digital system architecture is presented using Very High Speed Integrated Circuits Hardware Description Language (VHDL)and is implemented in FPGA chip. MATLAB ANN programming and tools are used for training the ANN. The trained weights are stored in different RAM, and is implemented in FPGA. The design was tested on a FPGA demo board. Keywords- Backpropagation, field programmable gate array (FPGA) hardware implementation, multilayer perceptron, pressure sensor, Xilinx FPGA.

Lightweight implementation of security primitives, e.g., physical unclonable functions (PUFs) and true random number generator, in field programmable gate array (FPGA) is crucial replacement of the conventional decryption key stored in battery-backed random access memory or E-Fuses for the protection of field reconfigurable assets. A slice is the smallest reconfigurable logic block in an Xilinx FPGA. The entropy exploitable from each slice of an FPGA is an important factor for the design of security primitives. Previous research has shown that the locations of slices can impact the quality of delay-based PUF designs implemented on FPGAs. To investigate the effect of the placement of each single-bit PUF cell free from the routing resource constraint between slices, single-bit ring oscillator (RO) and identity-based PUF design (Pi-coPUF) cells that can each be fully fitted into a single slice are evaluated. To accurately evaluate their statistical performance, data from a large number of devices are required. To this end, 217 Xilinx Artix-7 FPGAs has been employed to provide a large-scale comprehensive analysis for the two designs. This is the first time single-slice disorder-based security entities have been investigated and compared on 28-nm Xilinx FPGA. Uniqueness, uniformity, correlation, reliability, bit-aliasing and min-entropy of each type of cell are evaluated for four different types of cell placement. Our experimental results corroborate that the location of both cell types in the FPGA affects their performances. For both cell types, the lower the correlation between devices, the higher the min-entropy and uniqueness. Overall, the min-entropy, correlation and uniqueness of PicoPUF are slightly higher than those of RO. Otherwise, the uniformity, bit-aliasing and reliability of the PicoPUF are slightly lower than those of the RO. Comparing the resource usage and metrics of the PicoPUF, ring oscillator PUF and some existing memory-based PUF implementations, PicoPUF stands out as a lightweight FPGA-based weak PUF design. The raw data for the PicoPUF design are made publicly available to enable the research community to use them for benchmarking and/or validation.

We present a novel methodology that addresses the problem of faults in synapses of a spiking neural network using astrocyte regulation, inspired by recovery processes in the brain. Since Field Programmable Gate Arrays (FPGAs) are widely used for neural network applications, we aim to achieve fault tolerance in an astrocyte-neuron unit implemented on an FPGA. A fault is considered as a reduction in transmission probability of a synapse, leading to reduced spiking activity. Our novel repair mechanism exploits Dynamic Partial Reconfiguration (DPR) of the FPGA Clock Management Tiles (CMTs) to increase the clock frequency of neurons with reduced synaptic input, which restores the firing rate to pre-fault levels. The system maintains effective functional behavior with a loss of up to 90% of the original synaptic inputs to a neuron. Our repair mechanism has minimal hardware footprints with the repair unit which consumes only 0.8215% of the complete design and therefore supports scalable implementations. Additionally, the impact on power consumption of the design is also minimal (1.371W). The work opens up a novel way to utilize the capabilities of modern hardware to mimic homeostatic self-repair behavior achieving fault recovery.