Evolvable Reconfigurable Hardware Framework for Edge Detection

Systems on Reconfigurable Chips contain rich resources of logic, memory, and processor cores on the same fabric. This platform is suitable for implementation of Evolvable Reconfigurable Hardware Architectures (ERHA). It is based on the idea of combining reconfigurable Field Programmable Gate Arrays (FPGA) along with genetic algorithms (GA) to perform the reconfiguration operation. This architecture is a suitable candidate for implementation of early-processing stage operators of image processing such as filtering and edge detection. However, there are still fundamental issues need to be solved regarding the on-chip reprogramming of the logic. This paper presents a framework for implementing an evolvable hardware architecture for edge detection on Xilinx Virtex–4 chip. Some preliminary results are discussed

Self-Reconfigurable Analog Arrays: Off-The Shelf Adaptive Electronics for Space Applications

Development of analog electronic solutions for space avionics is expensive and lengthy. Lack of flexible analog devices, counterparts to digital Field Programmable Gate Arrays (FPGA), prevents analog designers from benefits of rapid prototyping. This forces them to expensive and lengthy custom design, fabrication, and qualification of application specific integrated circuits (ASIC). The limitations come from two directions: commercial Field Programmable Analog Arrays (FPAA) have limited variability in the components offered on-chip; and they are only qualified for best case scenarios for military grade (-55C to +125C). In order to avoid huge overheads, there is a growing trend towards avoiding thermal and radiation protection by developing extreme environment electronics, which maintain correct operation while exposed to temperature extremes (-180degC to +125degC). This paper describes a recent FPAA design, the Self-Reconfigurable Analog Array (SRAA) developed at JPL. It overcomes both limitations, offering a variety of analog cells inside the array together with the possibility of self-correction at extreme temperatures

Bridging the gap: rewritable electronics using real-time light-induced dielectrophoresis on lithium niobate

In the context of micro-electronics, the real-time manipulation and placement of components using optics alone promises a route towards increasingly dynamic systems, where the geometry and function of the device is not fixed at the point of fabrication. Here, we demonstrate physically reconfigurable circuitry through light-induced dielectrophoresis on lithium niobate. Using virtual electrodes, patterned by light, to trap, move, and chain individual micro-solder-beads in real-time via dielectrophoresis, we demonstrate rewritable electrical contacts which can make electrical connections between surface-bound components. The completed micro-solder-bead bridges were found to have relatively low resistances that were not solely dominated by the number of interfaces, or the number of discrete beads, in the connection. Significantly, these connections are formed without any melting/fusing of the beads, a key feature of this technique that enables reconfigurability. Requiring only a low-power (~3.5 mW) laser source to activate, and without the need for external power supply or signal generation, the all-optical simplicity of virtual-electrodes may prove significant for the future development of reconfigurable electronic systems

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

Evolvable circuit with transistor-level reconfigurability

An evolvable circuit includes a plurality of reconfigurable switches, a plurality of transistors within a region of the circuit, the plurality of transistors having terminals, the plurality of transistors being coupled between a power source terminal and a power sink terminal so as to be capable of admitting power between the power source terminal and the power sink terminal, the plurality of transistors being coupled so that every transistor terminal to transistor terminal coupling within the region of the circuit comprises a reconfigurable switch

Intrinsically Evolvable Artificial Neural Networks

Dedicated hardware implementations of neural networks promise to provide faster, lower power operation when compared to software implementations executing on processors. Unfortunately, most custom hardware implementations do not support intrinsic training of these networks on-chip. The training is typically done using offline software simulations and the obtained network is synthesized and targeted to the hardware offline. The FPGA design presented here facilitates on-chip intrinsic training of artificial neural networks. Block-based neural networks (BbNN), the type of artificial neural networks implemented here, are grid-based networks neuron blocks. These networks are trained using genetic algorithms to simultaneously optimize the network structure and the internal synaptic parameters. The design supports online structure and parameter updates, and is an intrinsically evolvable BbNN platform supporting functional-level hardware evolution. Functional-level evolvable hardware (EHW) uses evolutionary algorithms to evolve interconnections and internal parameters of functional modules in reconfigurable computing systems such as FPGAs. Functional modules can be any hardware modules such as multipliers, adders, and trigonometric functions. In the implementation presented, the functional module is a neuron block. The designed platform is suitable for applications in dynamic environments, and can be adapted and retrained online. The online training capability has been demonstrated using a case study. A performance characterization model for RC implementations of BbNNs has also been presented

An Evolvable Combinational Unit for FPGAs

A complete hardware implementation of an evolvable combinational unit for FPGAs is presented. The proposed combinational unit consisting of a virtual reconfigurable circuit and evolutionary algorithm was described in VHDL independently of a target platform, i.e. as a soft IP core, and realized in the COMBO6 card. In many cases the unit is able to evolve (i.e. to design) the required function automatically and autonomously, in a few seconds, only on the basis of interactions with an environment. A number of circuits were successfully evolved directly in the FPGA, in particular, 3-bit multipliers, adders, multiplexers and parity encoders. The evolvable unit was also tested in a simulated dynamic environment and used to design various circuits specified by randomly generated truth tables

On-Chip Intrinsic Evolution Methodology for Sequential Logic Circuit Design

This paper focuses on the application of Virtual Reconfigurable Circuit (VRC) design methodology and intrinsic evolution for the design of small sequential circuits and their implementation on a single programmable chip with an embedded hardcore processor. The evolutionary algorithm is developed in software that runs on the embedded processor. Fitness function is calculated using hardware architecture and is used to guide the evolution process. This new method is applied to the development of a 3-bit sequence detector and the evolved architecture is implemented on a Xilinx™ Virtex-II pro device. Simulations were run on the evolved architecture and on the same circuit designed using conventional Hardware Descriptive Language (HDL). Both designs showed the same functional behavior. Synthesis results show that the new method can be used in successfully implementing small sequential circuits on a reconfigurable hardware environment

A new project to address run-time reconfigurable hardware systems

Last autumn, we started a new project named Context Switching Reconfigurable Hardware for Communication Systems (COSRECOS). In this talk, I would like to present how we plan to address the challenge of changing hardware configurations while a system is in operation. The overall goal of the project is to contribute in making run-time reconfigurable systems more feasible in general. This includes introducing architectures for reducing reconfiguration time as well as undertaking tool development. Case studies by applications in network and communication systems will be a part of the project. Comments to the planned outline are much welcome