Generalized disjunction decomposition for evolvable hardware

Evolvable hardware (EHW) refers to self-reconfiguration hardware design, where the configuration is under the control of an evolutionary algorithm (EA). One of the main difficulties in using EHW to solve real-world problems is scalability, which limits the size of the circuit that may be evolved. This paper outlines a new type of decomposition strategy for EHW, the “generalized disjunction decomposition” (GDD), which allows the evolution of large circuits. The proposed method has been extensively tested, not only with multipliers and parity bit problems traditionally used in the EHW community, but also with logic circuits taken from the Microelectronics Center of North Carolina (MCNC) benchmark library and randomly generated circuits. In order to achieve statistically relevant results, each analyzed logic circuit has been evolved 100 times, and the average of these results is presented and compared with other EHW techniques. This approach is necessary because of the probabilistic nature of EA; the same logic circuit may not be solved in the same way if tested several times. The proposed method has been examined in an extrinsic EHW system using the$(1 + lambda)$evolution strategy. The results obtained demonstrate that GDD significantly improves the evolution of logic circuits in terms of the number of generations, reduces computational time as it is able to reduce the required time for a single iteration of the EA, and enables the evolution of larger circuits never before evolved. In addition to the proposed method, a short overview of EHW systems together with the most recent applications in electrical circuit design is provided

Design synthesis for dynamically reconfigurable logic systems

Dynamic reconfiguration of logic circuits has been a research problem for over four decades. While applications using logic reconfiguration in practical scenarios have been demonstrated, the design of these systems has proved to be a difficult process demanding the skills of an experienced reconfigurable logic design expert. This thesis proposes an automatic synthesis method which relieves designers of some of the difficulties associated with designing partially dynamically reconfigurable systems. A new design abstraction model for reconfigurable systems is proposed in order to support design exploration using the presented method. Given an input behavioural model, a technology server and a set of design constraints, the method will generate a reconfigurable design solution in the form of a 3D floorplan and a configuration schedule. The approach makes use of genetic algorithms. It facilitates global optimisation to accommodate multiple design objectives common in reconfigurable system design, while making realistic estimates of configuration overheads and of the potential for resource sharing between configurations. A set of custom evolutionary operators has been developed to cope with a multiple-objective search space. Furthermore, the application of a simulation technique verifying the lll results of such an automatic exploration is outlined in the thesis. The qualities of the proposed method are evaluated using a set of benchmark designs taking data from a real reconfigurable logic technology. Finally, some extensions to the proposed method and possible research directions are discussed

Design and application of reconfigurable circuits and systems

Open Acces

Reconfigurable Architectures and Systems for IoT Applications

abstract: Internet of Things (IoT) has become a popular topic in industry over the recent years, which describes an ecosystem of internet-connected devices or things that enrich the everyday life by improving our productivity and efficiency. The primary components of the IoT ecosystem are hardware, software and services. While the software and services of IoT system focus on data collection and processing to make decisions, the underlying hardware is responsible for sensing the information, preprocess and transmit it to the servers. Since the IoT ecosystem is still in infancy, there is a great need for rapid prototyping platforms that would help accelerate the hardware design process. However, depending on the target IoT application, different sensors are required to sense the signals such as heart-rate, temperature, pressure, acceleration, etc., and there is a great need for reconfigurable platforms that can prototype different sensor interfacing circuits. This thesis primarily focuses on two important hardware aspects of an IoT system: (a) an FPAA based reconfigurable sensing front-end system and (b) an FPGA based reconfigurable processing system. To enable reconfiguration capability for any sensor type, Programmable ANalog Device Array (PANDA), a transistor-level analog reconfigurable platform is proposed. CAD tools required for implementation of front-end circuits on the platform are also developed. To demonstrate the capability of the platform on silicon, a small-scale array of 24×25 PANDA cells is fabricated in 65nm technology. Several analog circuit building blocks including amplifiers, bias circuits and filters are prototyped on the platform, which demonstrates the effectiveness of the platform for rapid prototyping IoT sensor interfaces. IoT systems typically use machine learning algorithms that run on the servers to process the data in order to make decisions. Recently, embedded processors are being used to preprocess the data at the energy-constrained sensor node or at IoT gateway, which saves considerable energy for transmission and bandwidth. Using conventional CPU based systems for implementing the machine learning algorithms is not energy-efficient. Hence an FPGA based hardware accelerator is proposed and an optimization methodology is developed to maximize throughput of any convolutional neural network (CNN) based machine learning algorithm on a resource-constrained FPGA.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

FPGA acceleration of DNA sequence alignment: design analysis and optimization

Existing FPGA accelerators for short read mapping often fail to utilize the complete biological information in sequencing data for simple hardware design, leading to missed or incorrect alignment. In this work, we propose a runtime reconfigurable alignment pipeline that considers all information in sequencing data for the biologically accurate acceleration of short read mapping. We focus our efforts on accelerating two string matching techniques: FM-index and the Smith-Waterman algorithm with the affine-gap model which are commonly used in short read mapping. We further optimize the FPGA hardware using a design analyzer and merger to improve alignment performance. The contributions of this work are as follows. 1. We accelerate the exact-match and mismatch alignment by leveraging the FM-index technique. We optimize memory access by compressing the data structure and interleaving the access with multiple short reads. The FM-index hardware also considers complete information in the read data to maximize accuracy. 2. We propose a seed-and-extend model to accelerate alignment with indels. The FM-index hardware is extended to support the seeding stage while a Smith-Waterman implementation with the affine-gap model is developed on FPGA for the extension stage. This model can improve the efficiency of indel alignment with comparable accuracy versus state-of-the-art software. 3. We present an approach for merging multiple FPGA designs into a single hardware design, so that multiple place-and-route tasks can be replaced by a single task to speed up functional evaluation of designs. We first experiment with this approach to demonstrate its feasibility for different designs. Then we apply this approach to optimize one of the proposed FPGA aligners for better alignment performance.Open Acces

Smart technologies for effective reconfiguration: the FASTER approach

Current and future computing systems increasingly require that their functionality stays flexible after the system is operational, in order to cope with changing user requirements and improvements in system features, i.e. changing protocols and data-coding standards, evolving demands for support of different user applications, and newly emerging applications in communication, computing and consumer electronics. Therefore, extending the functionality and the lifetime of products requires the addition of new functionality to track and satisfy the customers needs and market and technology trends. Many contemporary products along with the software part incorporate hardware accelerators for reasons of performance and power efficiency. While adaptivity of software is straightforward, adaptation of the hardware to changing requirements constitutes a challenging problem requiring delicate solutions. The FASTER (Facilitating Analysis and Synthesis Technologies for Effective Reconfiguration) project aims at introducing a complete methodology to allow designers to easily implement a system specification on a platform which includes a general purpose processor combined with multiple accelerators running on an FPGA, taking as input a high-level description and fully exploiting, both at design time and at run time, the capabilities of partial dynamic reconfiguration. The goal is that for selected application domains, the FASTER toolchain will be able to reduce the design and verification time of complex reconfigurable systems providing additional novel verification features that are not available in existing tool flows