OPTIMAL AREA AND PERFORMANCE MAPPING OF K-LUT BASED FPGAS

FPGA circuits are increasingly used in many fields: for rapid prototyping of new products (including fast ASIC implementation), for logic emulation, for producing a small number of a device, or if a device should be reconfigurable in use (reconfigurable computing). Determining if an arbitrary, given wide, function can be implemented by a programmable logic block, unfortunately, it is generally, a very difficult problem. This problem is called the Boolean matching problem. This paper introduces a new implemented algorithm able to map, both for area and performance, combinational networks using k-LUT based FPGAs.k-LUT based FPGAs, combinational circuits, performance-driven mapping.

A dynamic reconfigurable architecture for hybrid spiking and convolutional FPGA-based neural network designs

This work presents a dynamically reconfigurable architecture for Neural Network (NN) accelerators implemented in Field-Programmable Gate Array (FPGA) that can be applied in a variety of application scenarios. Although the concept of Dynamic Partial Reconfiguration (DPR) is increasingly used in NN accelerators, the throughput is usually lower than pure static designs. This work presents a dynamically reconfigurable energy-efficient accelerator architecture that does not sacrifice throughput performance. The proposed accelerator comprises reconfigurable processing engines and dynamically utilizes the device resources according to model parameters. Using the proposed architecture with DPR, different NN types and architectures can be realized on the same FPGA. Moreover, the proposed architecture maximizes throughput performance with design optimizations while considering the available resources on the hardware platform. We evaluate our design with different NN architectures for two different tasks. The first task is the image classification of two distinct datasets, and this requires switching between Convolutional Neural Network (CNN) architectures having different layer structures. The second task requires switching between NN architectures, namely a CNN architecture with high accuracy and throughput and a hybrid architecture that combines convolutional layers and an optimized Spiking Neural Network (SNN) architecture. We demonstrate throughput results from quickly reprogramming only a tiny part of the FPGA hardware using DPR. Experimental results show that the implemented designs achieve a 7× faster frame rate than current FPGA accelerators while being extremely flexible and using comparable resources

Neuraghe: Exploiting CPU-FPGA synergies for efficient and flexible CNN inference acceleration on zynQ SoCs

Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require human-level understanding of data, like image or speech recognition. However, their computational load is significant, motivating the development of CNN-specialized accelerators. This work presents NEURAghe, a flexible and efficient hardware/software solution for the acceleration of CNNs on Zynq SoCs. NEURAghe leverages the synergistic usage of Zynq ARM cores and of a powerful and flexible Convolution-Specific Processor deployed on the reconfigurable logic. The Convolution-Specific Processor embeds both a convolution engine and a programmable soft core, releasing the ARM processors from most of the supervision duties and allowing the accelerator to be controlled by software at an ultra-fine granularity. This methodology opens the way for cooperative heterogeneous computing: While the accelerator takes care of the bulk of the CNN workload, the ARM cores can seamlessly execute hard-to-accelerate parts of the computational graph, taking advantage of the NEON vector engines to further speed up computation. Through the companion NeuDNN SW stack, NEURAghe supports end-to-end CNN-based classification with a peak performance of 169GOps/s and an energy efficiency of 17GOps/W. Thanks to our heterogeneous computing model, our platform improves upon the state-of-the-art, achieving a frame rate of 5.5 frames per second (fps) on the end-to-end execution of VGG-16 and 6.6fps on ResNet-18

Fault-tolerant fpga for mission-critical applications.

One of the devices that play a great role in electronic circuits design, specifically safety-critical design applications, is Field programmable Gate Arrays (FPGAs). This is because of its high performance, re-configurability and low development cost. FPGAs are used in many applications such as data processing, networks, automotive, space and industrial applications. Negative impacts on the reliability of such applications result from moving to smaller feature sizes in the latest FPGA architectures. This increases the need for fault-tolerant techniques to improve reliability and extend system lifetime of FPGA-based applications. In this thesis, two fault-tolerant techniques for FPGA-based applications are proposed with a built-in fault detection region. A low cost fault detection scheme is proposed for detecting faults using the fault detection region used in both schemes. The fault detection scheme primarily detects open faults in the programmable interconnect resources in the FPGAs. In addition, Stuck-At faults and Single Event Upsets (SEUs) fault can be detected. For fault recovery, each scheme has its own fault recovery approach. The first approach uses a spare module and a 2-to-1 multiplexer to recover from any fault detected. On the other hand, the second approach recovers from any fault detected using the property of Partial Reconfiguration (PR) in the FPGAs. It relies on identifying a Partially Reconfigurable block (P_b) in the FPGA that is used in the recovery process after the first faulty module is identified in the system. This technique uses only one location to recover from faults in any of the FPGA’s modules and the FPGA interconnects. Simulation results show that both techniques can detect and recover from open faults. In addition, Stuck-At faults and Single Event Upsets (SEUs) fault can also be detected. Finally, both techniques require low area overhead

Methodology to Derive Resource Aware Context Adaptable Architectures for Field Programmable Gate Arrays

The design of a common architecture that can support multiple data-flow patterns (or contexts) embedded in complex control flow structures, in applications like multimedia processing, is particularly challenging when the target platform is a Field Programmable Gate Array (FPGA) with a heterogeneous mixture of device primitives. This thesis presents scheduling and mapping algorithms that use a novel area cost metric to generate resource aware context adaptable architectures. Results of a rigorous analysis of the methodology on multiple test cases are presented. Results are compared against published techniques and show an area savings and execution time savings of 46% each

Techniques for efficient regular expression matching across hardware architectures

Regular expression matching is a central task for many networking and bioinformatics applications. For example, network intrusion detection systems, which perform deep packet inspection to detect malicious network activities, often encode signatures of malicious traffic through regular expressions. Similarly, several bioinformatics applications perform regular expression matching to find common patterns, called motifs, across multiple gene or protein sequences. Hardware implementations of regular expression matching engines fall into two categories: memory-based and logic-based solutions. In both cases, the design aims to maximize the processing throughput and minimize the resources requirements, either in terms of memory or of logic cells. Graphical Processing Units (GPUs) offer a highly parallel platform for memory-based implementations, while Field Programmable Gate Arrays (FPGAs) support reconfigurable, logic-based solutions. In addition, Micron Technology has recently announced its Automata Processor, a memory-based, reprogrammable hardware device. From an algorithmic standpoint, regular expression matching engines are based on finite automata, either in their non-deterministic or in their deterministic form (NFA and DFA, respectively). Micron's Automata Processor is based on a proprietary Automata Network, which extends classical NFA with counters and boolean elements. In this work, we aim to implement highly parallel memory-based and logic-based regular expression matching solutions. Our contributions are summarized as follows. First, we implemented regular expression matching on GPU. In this process, we explored compression techniques and regular expression clustering algorithms to alleviate the memory pressure of DFA-based GPU implementations. Second, we developed a parser for Automata Networks defined through Micron's Automata Network Markup Language (ANML), a XML-based high-level language designed to program the Automata Processor. Specifically, our ANML parser first maps the Automata Networks to an

Null Convention Logic applications of asynchronous design in nanotechnology and cryptographic security

This dissertation presents two Null Convention Logic (NCL) applications of asynchronous logic circuit design in nanotechnology and cryptographic security. The first application is the Asynchronous Nanowire Reconfigurable Crossbar Architecture (ANRCA); the second one is an asynchronous S-Box design for cryptographic system against Side-Channel Attacks (SCA). The following are the contributions of the first application: 1) Proposed a diode- and resistor-based ANRCA (DR-ANRCA). Three configurable logic block (CLB) structures were designed to efficiently reconfigure a given DR-PGMB as one of the 27 arbitrary NCL threshold gates. A hierarchical architecture was also proposed to implement the higher level logic that requires a large number of DR-PGMBs, such as multiple-bit NCL registers. 2) Proposed a memristor look-up-table based ANRCA (MLUT-ANRCA). An equivalent circuit simulation model has been presented in VHDL and simulated in Quartus II. Meanwhile, the comparison between these two ANRCAs have been analyzed numerically. 3) Presented the defect-tolerance and repair strategies for both DR-ANRCA and MLUT-ANRCA. The following are the contributions of the second application: 1) Designed an NCL based S-Box for Advanced Encryption Standard (AES). Functional verification has been done using Modelsim and Field-Programmable Gate Array (FPGA). 2) Implemented two different power analysis attacks on both NCL S-Box and conventional synchronous S-Box. 3) Developed a novel approach based on stochastic logics to enhance the resistance against DPA and CPA attacks. The functionality of the proposed design has been verified using an 8-bit AES S-box design. The effects of decision weight, bitstream length, and input repetition times on error rates have been also studied. Experimental results shows that the proposed approach enhances the resistance to against the CPA attack by successfully protecting the hidden key --Abstract, page iii

Implementation and Evaluation of an NoC Architecture for FPGAs

The Networks-on-Chip (NoC) approach for designing Systems-on-Chip (SoC) is currently emerging as an advanced concept for overcoming the scalability and efficiency problems of traditional bus-based systems. A great deal of theoretical research has been done in this area that provides good insight and shows promising results. There is a great need for research in hardware implementation of NoC-based systems to determine the feasibility of implementing various topologies and protocols, and also to accurately determine what design tradeoffs are involved in NoC implementation. This thesis addresses the challenges of implementing an NoC-based system on FPGAs for running real benchmark applications. The NoC used a mesh topology and circuit-switched communication protocol. An experimental framework was developed that allowed implementation of NoC-based system from a high level specification, using the Celoxica Handel-C hardware description language. Two test applications: charged couple device (CCD) and JPEG were developed in Handel-C to be used as our benchmark applications. Both benchmarks are computational expensive and require large quantities of data transfer that will test the NoC system. Implementation results show that the NoC-based system gives superior area utilization and speed performance compared to the bus-based system, running the same benchmarks

Range-enhanced packet classification to improve computational performance on field programmable gate array

Multi-filed packet classification is a powerful classification engine that classifies input packets into different fields based on predefined rules. As the demand for the internet increases, efficient network routers can support many network features like quality of services (QoS), firewalls, security, multimedia communications, and virtual private networks. However, the traditional packet classification methods do not fulfill today’s network functionality and requirements efficiently. In this article, an efficient range enhanced packet classification (REPC) module is designed using a range bit-vector encoding method, which provides a unique design to store the precomputed values in memory. In addition, the REPC supports range to prefix features to match the packets to the corresponding header fields. The synthesis and implementation results of REPC are analyzed and tabulated in detail. The REPC module utilizes 3% slices on Artix-7 field programmable gate array (FPGA), works at 99.87 Gbps throughput with a latency of 3 clock cycles. The proposed REPC is compared with existing packet classification approaches with better hardware constraints improvements