Speed enhancement with soft computing hardware
We will review our work on electronic neural networks and evolvable hardware to bring out speed advantage.
Engineering topics
Publications and source records attributed to Daud, T..
We will review our work on electronic neural networks and evolvable hardware to bring out speed advantage.
The purpose of this paper is to illustrate evolution of analog circuits on a stand-alone board-level evolvable system (SABLES). SABLES is part of an effort to achieve integrated evolvable systems. SABLES provides autonomous, fast (tens to hundreds of seconds), on-chip circuit evolution involving about 100,000 circuit evaluations. Its main components are a JPL Field Programmable Transistor Array (FPTA) chip used as transistor-level reconfigurable hardware, and a TI DSP that implements the evolutionary algorithm controlling the FPTA reconfiguration. The paper details an example of evolution on SABLES and points out to certain transient and memory effects that affect the stability of solutions obtained reusing the same piece of hardware for rapid testing of individuals during evolution.
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We describe in this paper experiments on the evolution of current mode circuits.
Temperature tolerant electronics and long life survivability are key capabilities required for future NASA/JPL missions. Current approaches to electronics for extreme environments focus on component level robustness and hardening. Compensation techniques, e.g., as offered by bias cancellation circuits, have also been employed. This paper presents a novel approach, based on evolvable hardware technology, which allows adaptive in situ circuit redesign/reconfiguration during the operation in the environment. This technology would complement material/device advancements and bring closer the success of missions in harsh environments. Additional information is contained in the original extended abstract.
This paper discusses the use of Evolvable Hardware (EHW) in automatic synthesis of electronic circuits for computational intelligence (CI) hardware.
For over a decade, JPL has been actively involved in soft computing research on theory, architecture, applications, and electronics hardware. The driving force in all our research activities, in addition to the potential enabling technology promise, has been creation of a niche that imparts orders of magnitude speed advantage by implementation in parallel processing hardware with algorithms made especially suitable for hardware implementation. We review our work on neural networks, fuzzy logic, and evolvable hardware with selected application examples requiring real time response capabilities.
As the focus of our present paper, we have used the cascade error projection (CEP) learning algorithm (shown to be hardware-implementable) with on-chip learning (OCL) scheme to obtain three orders of magnitude speed-up in target recognition compared to software-based learning schemes. Thus, it is shown, real time learning as well as data processing for target recognition can be achieved.
Evolvable Hardware (EHW) refers to HW design and self-reconfiguration using evolutionary/genetic mechanisms. The paper presents an overview of some key concepts of EHW, describing also a set of selected applications.
A new concept in avionics development, X2000, is described.
The paper presents the concept and initial test from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor.
In this paper, we reinvestigate the solution for chaotic time series prediction problem using neural network approach. This study suggests that Cascade Error Projection (CEP) is an implementable learning technique for hardware consideration.
The paper presents the hardware implementation and initial tests from a low-power, high-speed reconfigurable sensor fusion processor.
In this paper, we workout a detailed mathematical analysis for a new learning algorithm termed Cascade Error Projection (CEP) and a general learning frame work.
The paper presents the concept and initial tests from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor. The Extended Logic Intelligent Processing System (ELIPS) processor is developed to seamlessly combine rule-based systems, fuzzy logic, and neural networks to achieve parallel fusion of sensor in compact low power VLSI.
This paper presents the system architecture and processing algorithms.
Algorithms for the solution of spatio-temporal recognition and classification problems are known to require intense image data computation.
High connectivity of artificial neural network chip-embodiments combined with currently emerging 3-dimensionally stacked multichip modules for real-time applications of target classification require a scrutiny for low power technology insertion.