Field Programmable Gate Array Family Research Articles

FEINT: Automated Framework for Efficient INsertion of Templates/Trojans into FPGAs

Field-Programmable Gate Arrays (FPGAs) play a significant and evolving role in various industries and applications in the current technological landscape. They are widely known for their flexibility, rapid prototyping, reconfigurability, and design development features. FPGA designs are often constructed as compositions of interconnected modules that implement the various features/functionalities required in an application. This work develops a novel tool FEINT, which facilitates this module composition process and automates the design-level modifications required when introducing new modules into an existing design. The proposed methodology is architected as a “template” insertion tool that operates based on a user-provided configuration script to introduce dynamic design features as plugins at different stages of the FPGA design process to facilitate rapid prototyping, composition-based design evolution, and system customization. FEINT can be useful in applications where designers need to tailor system behavior without requiring expert FPGA programming skills or significant manual effort. For example, FEINT can help insert defensive monitoring, adversarial Trojan, and plugin-based functionality enhancement features. FEINT is scalable, future-proof, and cross-platform without a dependence on vendor-specific file formats, thus ensuring compatibility with FPGA families and tool versions and being integrable with commercial tools. To assess FEINT’s effectiveness, our tests covered the injection of various types of templates/modules into FPGA designs. For example, in the Trojan insertion context, our tests consider diverse Trojan behaviors and triggers, including key leakage and denial of service Trojans. We evaluated FEINT’s applicability to complex designs by creating an FPGA design that features a MicroBlaze soft-core processor connected to an AES-accelerator via an AXI-bus interface. FEINT can successfully and efficiently insert various templates into this design at different FPGA design stages.

An efficient implementation of Hybrid Signcryption Processor using Flexible Encryption and Signature Techniques

In today’s digital era, with the enormous growth in the Internet of Things, everyday humans utilize IoT in various applications such as banking, online transactions, purchasing data forwarding, etc. Cryptography is all about the design and implementation of protocols that keep the data safe and secure from third parties. The design of the cryptographic processor present in the Field Programmable Gate Array (FPGA) incurs more power, area, and throughput. In this article, an Efficient Hybrid Signcryption Processor (EHSP) which operates based on the principle of encryption and signature is proposed. Generally, a hybrid signcryption operation comprises of Data Encapsulation Method (DEM) and Key Encapsulation Method (KEM). and in EHSP, we utilize the improved form of the Modified Kurosawa and Desmedt (MKD) hash scheme to encapsulate the key; the Elliptic Curve Cryptography (ECC) processor is used to encapsulate the message. The ECC processor is designed in a flexible manner and the flexibility is achieved by the flexible bit-serial multiplier, which reduces the power, and area consumption and maximizes the throughput. The proposed EHSP architecture is designed and simulated in Xilinx tool with various field programmable gate array families such as Virtex4 (XC4VLX60), Virtex5 (XC5VLX50), and Virtex7 (XC7V330T). The simulation result depicts the efficiency of the proposed EHSP design with respect to total power consumption, utilization of hardware, and throughput.

A novel reduced instruction set computer-communication processor design using field programmable gate array

In this paper, a novel reduced instruction set computer (RISC)- communication processor (RCP) has been designed with 32-bit operations which access 64-bit instruction format and implemented using field programmable gate array (FPGA). The design of the RISC processor is facilitated with communication operations like basic signals sine, cosine, and square, and modulation schemes like amplitude modulation, amplitude shift keying, analog, and digital quadrature amplitude modulation. Additionally, application-oriented operations like a traffic light, digital clock, and linear feedback shift register are included in the design. The pipeline mechanism is incorporated in the design to enhance the performance characteristics of the processor, hence allowing the execution of the instructions more effectively. Also, the design is implemented with Xilinx Virtex 7 family FPGA. The device utilization analysis of the proposed FPGA along with different FPGA families is evaluated and compared.

Error Detection Constructions for ITA Finite Field Inversions Over $\text{GF}(2^{m})$ on FPGA Using CRC and Hamming Codes

Finite field arithmetic operations over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{GF}(2^{m})$</tex-math></inline-formula> are widely used in critical applications, such as cryptography, coding theory, error-correcting codes, and digital signal processing. Finite field inversions are the most time-consuming operations among other widely-used ones and require a large footprint as well as power/energy to be performed. To reduce such complexity, the Itoh–Tsujii algorithm (ITA) has received prominent attention in the literature; however, implementations using ITA are still vulnerable to natural very-large-scale integration defects. To overcome the challenge of detecting naturally-induced faults in such constructions, for the first time, we propose error detection schemes based on Hamming codes for architectures performing finite field inversions using the ITA algorithm over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{GF}(2^{m})$</tex-math></inline-formula> with polynomial basis. Additionally, CRC-oriented error detection schemes for inversions in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\text{GF}(2^{m})$</tex-math></inline-formula> with normal basis are also studied and new approaches to protect them are presented. In this article, general formulations are provided along with different case studies to show the feasibility of our schemes with any finite field size. Moreover, field-programmable gate array (FPGA) implementations are performed on two Xilinx FPGA families, i.e., Kintex UltraScale+ and Xilinx Virtex-7 UltraScale+, to verify that the overheads added by the error detection architectures to provide reliability are suitable for deeply-constrained embedded systems.

Configuration Memory Scrubbing of SRAM-Based FPGAs Using a Mixed 2-D Coding Technique

SRAM-based field-programmable gate array (FPGA) vendors typically integrate error correction codes (ECCs) into the configuration memory to assist designers in implementing scrubbing mechanisms. In most cases, these ECC schemes guarantee the correction of single- and double-bit errors per configuration frame but fail to correct upsets with higher multiplicity in a single frame caused by a single event. This phenomenon has been observed in modern commercial-off-the-shelf FPGAs. Bit interleaving schemes are used in some FPGA families to scatter the multiple upsets into more than one frame, but this does not fully resolve the problem of uncorrectable errors. In this article, we propose a configuration memory scrubbing approach for SRAM-based FPGA devices, which combines the embedded ECC logic with an interframe, interleaved parity code to build a mixed 2-D coding technique. The proposed technique improves the multiple-bit error correction capabilities of the on-chip ECC scheme while keeping the error correction latency and hardware cost low. The scrubbing concept has been validated under heavy-ion irradiation, where it succeeded in correcting all the single and multiple upsets observed during the radiation experiment.

Fast Search and Efficient Placement Algorithm for Reconfigurable Tasks on Modern Heterogeneous FPGAs

To date, only a tiny fraction of reconfigurable task placement algorithms is targeted at modern heterogeneous field-programmable gate array (FPGA) architecture, and they often focus on determining the final placement location and pursuing placement quality. Hence, their real-time performance is poor because feasible location searching and placement speed are rarely taken into consideration. In this article, we propose a fast search strategy based on characteristic target gene sequence (CTGS) and an efficient placement algorithm called prioritization-based minimum cost and marginal compact (P2MC). CTGS ascertains tasks’ feasible locations quickly by regarding the relatively few heterogeneous resources on FPGAs as search targets. P2MC first introduces prioritization heuristics based on task characteristics (PHTC) to presort tasks in order to improve the placement success rate and then select the final location according to the principle of minimum cost and marginal compact (2MC) so as to reduce the fragmentation of free space. The proposed algorithms are verified and evaluated on Xilinx’s mainstream FPGA families Virtex-5/6/7. Results show that CTGS can accelerate the search speed of tasks’ feasible locations by about four to five times, and P2MC can further balance placement speed and success rate. Compared with state-of-the-art heterogeneous task placement algorithms, P2MC can either increase both placement speed and success rate (by about 29% and 4.5%, respectively) or significantly increase the placement speed (by 20 times) at the expense of a bit of placement success rate (by only 5.8%).

Distortion Analysis for Programmable Analog Signals Generated Using FPGA

Abstract Practical applications in electrical engineering often deal with analog signals, but there are employed also digital circuits and microcontrollers. Sometimes it is easier or cheaper to improvise DA and AD converters instead of using dedicated ones. This paper is a comparative analysis of how accurate the analog signals can be generated from digital systems, like Field Programmable Gate Arrays (FPGAs) using only additional passive circuits. For this analysis, different FPGAs families were tested. Two alternatives of generating analog signals were considered in this study: PWM and R2R network. The analysis has been performed for a common frequency band, from 100 Hz to 30 kHz, using a virtual instrument for building an automated test system. At 10 kHz the PWM module obtains better THD values, reaching even 0.013% and at 30 kHz the R2R module obtains better THD values, reaching even 0.222%.

Design and Implementation of Scalable and Parametrizable Analog-to-Digital Converter on FPGA

The flexibility provided by FPGAs permits the implementation of several ADCs, each one configured with the required bit resolution and sampling frequency. The paper presents the design and implementation of scalable and parametrizable analog-to-digital converters (ADC), based on a successive approximation register (SAR), on FPGAs (field programmable gate arrays). Firstly, the work develops a systematic methodology for the implementation of a parametrizable SAR-based ADC from a set of building modules, such as the pulse-width modulator (PWM), external low-pass filter (LPF) and the analog comparator. The presented method allows choosing the LPF parameters for the required performance (resolution bits and sampling frequency) of a SAR-based ADC. Secondly, the paper also presents several optimizations on the PWM module to enhance the sampling frequency of implemented ADCs, and the method to choose the LPF parameters is adapted. The PWM and SAR logic are synthesizable and parametrizable, using a low number of resources, in order to be portable for low-cost FPGA families. The methodology and PWM optimizations are tested on a Zynq-7000 device from Xilinx; however, they can be adapted to any other FPGA.

Hardware Implementation of Stockwell Transform and Smoothed Pseudo Wigner Ville Distribution Transform on FPGA using CORDIC Algorithm

A comparison of linear and quadratic transform implementation on field programmable gate array (FPGA) is presented. Popular linear transform namely Stockwell Transform and Smoothed Pseudo Wigner Ville Distribution (SPWVD) transform from Quadratic transforms is considered for the implementation on FPGA. Both the transforms are coded in Verilog hardware description language (Verilog HDL). Complex calculations of transformation are performed by using CORDIC algorithm. From FPGA family, Spartan-6 is chosen as hardware device to implement. Synthetic chirp signal is taken as input to test the both designed transforms. Summary of hardware resource utilization on Spartan-6 for both the transforms is presented. Finally, it is observed that both the transforms S-Transform and SPWVD are computed with low elapsed time with respect to MATLAB simulation.

Enhanced MAC controller architecture for 2D processing based on FPGA with configurable resource allocation

The bulks of image processing algorithms are either two-dimensional (2D) or confined by their very nature. As a result, the 2D convolution function has a large impact on picture processing requirements. The methodology of 2D convolution and media access control (MAC) design can also be used to perform a variety of image processing tasks, and even as picture blurring, softening, and feature extraction. The main goal of this research is to develop a more efficient MAC control block-based 2D convolution architecture. This 2D algorithm can be implemented in hardware using fewer modules, multipliers, adders, and control blocks, resulting in significant hardware savings and look up table (LUT) reductions. The simulations were run in Verilog, and the Xilinx Vertex family field programmable gate array (FPGA) was used to build and test them. The recommended 2D convolution architectural solution is significantly faster and consumes significantly less hardware resources than the traditional 2D convolution implementation. The proposed architecture will result in technology that saves a substantial amount of processing time when it comes to LUTs.

Identifying Radiation-Induced Micro-SEFIs in SRAM FPGAs

Field-programmable gate arrays (FPGAs) are susceptible to radiation-induced effects that can affect more than one memory cell. Radiation-induced microsingle event functional interrupts (micro-SEFIs) are one of such events that can upset several bits at a time. These events need to be studied because they can overcome protection from techniques such as triple modular redundancy (TMR) and error correction codes (ECCs). Extracting these events from radiation data helps to understand if specific resources of the FPGA are more vulnerable and the extent of this vulnerability. This article presents a method based on statistics and fault injection to identify micro-SEFIs from beam-test data in the configuration memory and block RAM (BRAM) of SRAM-based FPGAs. The results show the cross section of these events for the configuration RAM (CRAM) and BRAM for three families of Xilinx SRAM FPGAs gathered throughout three neutron tests. This article also contains data from a fault injection campaign to uncover the possible CRAM source bits causing micro-SEFIs in memory look-up tables (LUTs) of Xilinx 7-series and Ultrascale devices.

Ultra-Lightweight Block Cipher in Medical Internet of Things for Secure Machine-to-Machine Communication Using FPGA

With the swift growth of internet applications with the Internet of Things (IoT), medical sensors and online medical service has become mandatory part of every use case in the recent years. The health care system has become more connected with the increasing use of IoT devices, and these medical devices introduce vulnerabilities into health care organizations. Recently, there are significant cyber-threats against the medical IoT and some resisting elements of cryptosystem that cloud help to combat these threats. Hence, in medical IoT, security and privacy of patients are among the major areas of concern. However, such methods are very complex to implement in Field Programmable Gate Array (FPGA) platforms. This paper proposes an ultra-lightweight block ciphers (ULBC) for medical IoT applications. The first contribution of ULBC algorithm is to propose the chaotic Whale optimization (CWO) algorithm for key management, which improves the security and reduce hardware cost by reducing the number of iteration rounds. The second contribution is to introduce the flexible design of modified ULBC algorithm that provides an area, power and delay efficient design with attack free feature. The flexible design avoids the reconfigurable runtime, power; and it is surely different from existing block ciphers. Finally, the ULBC algorithm is synthesized in Xilinx tool with different FPGA families and the performance is compared with existing lightweight ciphers in terms of maximum clock frequency, power consumption and hardware utilization.

Argus CNN Accelerator Based on Kernel Clustering and Resource-Aware Pruning

Paper proposes a two-step Convolutional Neural Network (CNN) pruning algorithm and resource-efficient Field-programmable gate array (FPGA) CNN accelerator named “Argus”. The proposed CNN pruning algorithm first combines similar kernels into clusters, which are then pruned using the same regular pruning pattern. The pruning algorithm is carefully tailored for FPGAs, considering their resource characteristics. Regular sparsity results in high Multiply-accumulate (MAC) efficiency, reducing the amount of logic required to balance workloads among different MAC units. As a result, the Argus accelerator requires about 170 Look-up tables (LUTs) per Digital Signal Processor (DSP) block. This number is close to the average LUT/DPS ratio for various FPGA families, enabling balanced resource utilization when implementing Argus. Benchmarks conducted using Xilinx Zynq Ultrascale + Multi-Processor System-on-Chip (MPSoC) indicate that Argus is achieving up to 25 times higher frames per second than NullHop, 2 and 2.5 times higher than NEURAghe and Snowflake, respectively, and 2 times higher than NVDLA. Argus shows comparable performance to MIT’s Eyeriss v2 and Caffeine, requiring up to 3 times less memory bandwidth and utilizing 4 times fewer DSP blocks, respectively. Besides the absolute performance, Argus has at least 1.3 and 2 times better GOP/s/DSP and GOP/s/Block-RAM (BRAM) ratios, while being competitive in terms of GOP/s/LUT, compared to some of the state-of-the-art solutions.

Development and Testing on the European Space-Grade BRAVE FPGAs: Evaluation of NG-Large Using High-Performance DSP Benchmarks

The advent of space applications with increased computational requirements has led the space industry to consider innovative chips and avionics architectures for high-performance on-board data processing. In a relatively limited market, the European BRAVE family of Field-Programmable Gate Arrays (FPGAs) offers such novel radiation-hardened solutions. Towards verification, the current work devises and applies a methodology to thoroughly assess the BRAVE FPGAs and their SW tools. The paper focuses on NG-Large, i.e., the largest FPGA of the 65nm Radiation-Hardened-By-Design (RHBD) technology of NanoXplore to date. The proposed approach comprises a number of customized steps to systematically evaluate the entire FPGA design flow. Initially, we carefully select and tune a set of high-performance Digital Signal Processing (DSP) & Computer Vision (CV) benchmarks, which were originally developed as Hardware Description Language (HDL) IPs in past projects of the European Space Agency (ESA). Subsequently, we perform exhaustive exploration of the Synthesis, Placement, and Routing stages of the SW tools, as well as testing on actual HW boards. At each step, we generate and analyze a variety of results, while we also compare them to 3rd-party solutions. The results show that NG-Large provides sufficient programmability and performance, e.g., classic CV IPs for feature detection on megapixel images can achieve a throughput of 5–10 frames per second, while the on-chip memory utilization is up to 56% better than that of 3rd-party FPGAs. As a highlight, at system-level, we successfully implement and execute an entire HW/SW algorithmic pipeline for Vision-Based Navigation (VBN) involving SpaceWire data transfers with LEON CPU & NG-Large co-processing.

LUT‐based high‐speed point multiplier for Goldilocks‐Curve448

Recent studies have shown that existing elliptic curve-based cryptographic standards provide backdoors for manipulation and hence compromise the security. In this regard, two new elliptic curves known as Curve448 and Curve25519 are recently recommended by IETF for transport layer security future generations. Hence, cryptosystems built over these elliptic curves are expected to play a vital role in the near future for secure communications. A high-speed elliptic curve cryptographic processor (ECCP) for the Curve448 is proposed in this study. The area of the ECCP is optimised by performing different modular operations required for the elliptic curve Diffie–Hellman protocol through a unified architecture. The critical path delay of the proposed ECCP is optimised by adopting the redundant-signed-digit technique for arithmetic operations. The segmentation approach is introduced to reduce the required number of clock cycles for the ECCP. The proposed ECCP is developed using look-up-tables (LUTs) only, and hence it can be ported to any field-programmable gate array family or standard ASIC libraries. The authors' ECCP design offers higher speed without any significant area overhead to recent designs reported in the literature.

Achieving Picosecond-Level Phase Stability in Timing Distribution Systems With Xilinx Ultrascale Transceivers

This article discusses the challenges posed on the field-programmable gate array (FPGA) transceivers in terms of phase-determinism requirements for timing distribution at the Large Hadron Collider (LHC) experiments. Having a fixed phase after startups is a major requirement, and the typical phase variations observed in the order of tens of picoseconds after startups while using the state-of-the-art design techniques are no longer sufficient. Each limitation observed in the transmitter and receiver paths of the high-speed transceivers embedded in the Xilinx Ultrascale FPGA family is further investigated and solutions are proposed. Tests in hardware using Xilinx FPGA evaluation boards are presented. In addition to a higher phase determinism, the techniques presented make it possible to fine-tune the skew of a link with a picosecond resolution, greatly simplifying clock-domain crossing inside the FPGAs and providing better short-term stability for the FPGA-recovered clock in a high-speed link.

An In-Sight Into How Compression Dictionary Architecture Can Affect the Overall Performance in FPGAs

This paper presents a detailed analysis of various approaches to hardware implemented compression algorithm dictionaries, including our optimized method. To obtain comprehensive and detailed results, we introduced a method for the fair comparison of programmable hardware architectures to show the benefits of our approach from the perspective of logic resources, frequency, and latency. We compared two generally used methods with our optimized method, which was found to be more suitable for maintaining the memory content via (in)valid bits in any mid-density memory structures, which are implemented in programmable hardware such as FPGAs (Field Programmable Gate Array). The benefits of our new method based on a “Distributed Memory” technique are shown on a particular example of compression dictionary but the method is also suitable for another use cases requiring a fast (re-)initialization of the used memory structures before each run of an algorithm with minimum time and logic resources consumption. The performance evaluation of the respective approaches has been made in Xilinx ISE and Xilinx Vivado toolkits for the Virtex-7 FPGA family. However the proposed approach is compatible with 99% of modern FPGAs.

FPGA Implementation of L1/2 Sparsity Constrained Nonnegative Matrix Factorization Algorithm for Remotely Sensed Hyperspectral Image Analysis

Remotely sensed hyperspectral images provide data of the earth’s surface components. The data provided is collected through airborne devices such as satellites with the capability to collect large amounts of data to be sent to ground stations for processing. The main disadvantage of this scenario is the limited bandwidth connection between the airborne devices and the ground station on Earth which affects the information sending and real time processing. A possible solution is to include an on-board data processor. Field-Programmable Gate Arrays (FPGAs) are excellent target platform that allows the design reconfigurability, powerful computing and high performance levels. One of the most commonly used techniques in hyperspectral data analysis is linear spectral unmixing. In the last decade, L1/2 sparsity constrained Nonnegative Matrix Factorization (NMF), a linear spectral unmixing algorithm, and its extensions have been heavily studied to unmix the hyperspectral images and recover their material spectra. L1/2 regularizer is proven to have much better results in terms of sparsity and accuracy than other regularizers yet, to the best of our knowledge, has not been implemented. In this paper, we present an FPGA design for the L1/2 sparsity constrained NMF (L1/2-NMF) algorithm. The proposed design is tested on both synthetic and real data sets and implemented on Altera Family FPGAs. Implementation results show that the proposed design successfully unmixes the data with maximum frequency of 52.6 MHz and a speedup factor of 3.9 for the synthetic data set and a frequency of 104.32 MHz and a speedup factor of 1.14 for the real data set. The implementation results are compared to the simulation results and ground truth signatures using Spectral Angular Distance (SAD) measure. Calculations show that the implementation results have comparable SAD values to the simulation results.

A Simple Multilevel Space Vector Modulation Technique and MATLAB System Generator Built FPGA Implementation for Three-Level Neutral-Point Clamped Inverter

The pulse width modulation (PWM) is an important segment in power electronic inverters and multilevel inverters (MLIs) design. The space vector modulation (SVM) methods own distinct advantages over other PWM methods. However, MLI SVM has involved more mathematics in their executions. Hence, the digital signal processors (DSPs) or field programmable gate arrays (FPGAs) based digital implementations are highly preferred for MLI SVM realizations, which require exceptional properties. The conventional MLI SVMs use complex mathematical functions to solve their internal functions to identify the space vector diagram (SVD) sub-triangle and over modulation boundary switching on-times. Particularly these are the changes in the position of reference vector with respect to their sub-triangle positions involving higher mathematical functions. This paper proposes a simplified three-level MLI SVM that reduces the sub-triangle and over modulation switching on-time calculations with reduced mathematical functions. The proposed MLI SVM is derived based on two-level SVM without changing the reference vector position, unlike the traditional approaches. This helps in extending the SVM for any n-level inverter with additional LUTs. The detailed theoretical study, MATLAB-Simulink system generator simulations and Xilinx FPGA family SPARTAN-III-3A based experimental implementations are done with three-level neutral point MLI fed induction motor drive. The theoretical design, analysis, and experimentation results validate the advantages of the proposed PWM design and its implementation. In addition, the proposed implementation is executed from the MATLAB Xilinx system generator directly into target FPGA, which makes it faithful, efficient and minimizes the time spent.

An FPGA-Based Hardware Accelerator for CNNs Using On-Chip Memories Only: Design and Benchmarking with Intel Movidius Neural Compute Stick

During the last years, convolutional neural networks have been used for different applications, thanks to their potentiality to carry out tasks by using a reduced number of parameters when compared with other deep learning approaches. However, power consumption and memory footprint constraints, typical of on the edge and portable applications, usually collide with accuracy and latency requirements. For such reasons, commercial hardware accelerators have become popular, thanks to their architecture designed for the inference of general convolutional neural network models. Nevertheless, field-programmable gate arrays represent an interesting perspective since they offer the possibility to implement a hardware architecture tailored to a specific convolutional neural network model, with promising results in terms of latency and power consumption. In this article, we propose a full on-chip field-programmable gate array hardware accelerator for a separable convolutional neural network, which was designed for a keyword spotting application. We started from the model implemented in a previous work for the Intel Movidius Neural Compute Stick. For our goals, we appropriately quantized such a model through a bit-true simulation, and we realized a dedicated architecture exclusively using on-chip memories. A benchmark comparing the results on different field-programmable gate array families by Xilinx and Intel with the implementation on the Neural Compute Stick was realized. The analysis shows that better inference time and energy per inference results can be obtained with comparable accuracy at expenses of a higher design effort and development time through the FPGA solution.