Vlsi Implementation of Olfactory Cortex Model

This thesis attempts to implement the building blocks required for the realization of the biologically motivated olfactory neural model in silicon as the special purpose hardware. The olfactory model is originally developed by R. Granger, G. Lynch, and Ambros-Ingerson. CMOS analog integrated circuits were used for this purpose. All of the building blocks were fabricated using the MOSIS service and tested at our site. The results of this study can be used to realize a system level integration of the olfactory model.Electrical Engineerin

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

Inter-chip communications in an analogue neural network utilising frequency division multiplexing

As advances have been made in semiconductor processing technology, the number of transistors on a chip has increased out of step with the number of input/output pins, which has introduced a communications ’bottle-neck’ in the design of computer architectures. This is a major issue in the hardware design of parallel structures implemented in either digital or analogue VLSI, and is particularly relevant to the design of neural networks which need to be highly interconnected. This work reviews hardware implementations of neural networks, with an emphasis on analogue implementations, and proposes a new method for overcoming connectivity constraints, by the use of Frequency Division Multiplexing (FDM) for the inter-chip communications. In this FDM scheme, multiple analogue signals are transmitted between chips on a single wire by modulating them at different frequencies. The main theoretical work examines the number of signals which can be packed into an FDM channel, depending on the quality factors of the filters used for the demultiplexing, and a fractional overlap parameter which was defined to take into account the inevitable overlapping of filter frequency responses. It is seen that by increasing the amount of permissible overlap, it is possible to communicate a larger number of signals in a given bandwidth. Alternatively, the quality factors of the filters can be reduced, which is advantageous for hardware implementation. Therefore, it was found necessary to determine the amount of overlap which might be permissible in a neural network implementation utilising FDM communications. A software simulator is described, which was designed to test the effects of overlap on Multilayer Perceptron neural networks. Results are presented for networks trained with the backpropagation algorithm, and with the alternative weight perturbation algorithm. These were carried out using both floating point and quantised weights to examine the combined effects of overlap and weight quantisation. It is shown using examples of classification problems, that the neural network learning is indeed highly tolerent to overlap, such that the effect on performance (i.e. on convergence or generalisation) is negligible for fractional overlaps of up to 30%, and some tolerence is achieved for higher overlaps, before failure eventually occurs. The results of the simulations are followed up by a closer examination of the mechanism of network failure. The last section of the thesis investigates the VLSI implementation of the FDM scheme, and proposes the use of the operational transconductance amplifier (OTA) as a building block for implementation of the FDM circuitry in analogue VLSI. A full custom VLSI design of an OTA is presented, which was designed and fabricated through Eurochip, using HSPICE/Mentor Graphics CAD tools and the Mietec 2.4µ CMOS process. A VLSI architecture for inter-chip FDM is also proposed, using adaptive tuning of the OTA-C filters and oscillators.This forms the basis for a program of further work towards the VLSI realisation of inter-chip FDM, which is outlined in the conclusions chapter

Inter-chip communications in an analogue neural network utilising frequency division multiplexing

As advances have been made in semiconductor processing technology, the number of transistors on a chip has increased out of step with the number of input/output pins, which has introduced a communications ’bottle-neck’ in the design of computer architectures. This is a major issue in the hardware design of parallel structures implemented in either digital or analogue VLSI, and is particularly relevant to the design of neural networks which need to be highly interconnected. This work reviews hardware implementations of neural networks, with an emphasis on analogue implementations, and proposes a new method for overcoming connectivity constraints, by the use of Frequency Division Multiplexing (FDM) for the inter-chip communications. In this FDM scheme, multiple analogue signals are transmitted between chips on a single wire by modulating them at different frequencies. The main theoretical work examines the number of signals which can be packed into an FDM channel, depending on the quality factors of the filters used for the demultiplexing, and a fractional overlap parameter which was defined to take into account the inevitable overlapping of filter frequency responses. It is seen that by increasing the amount of permissible overlap, it is possible to communicate a larger number of signals in a given bandwidth. Alternatively, the quality factors of the filters can be reduced, which is advantageous for hardware implementation. Therefore, it was found necessary to determine the amount of overlap which might be permissible in a neural network implementation utilising FDM communications. A software simulator is described, which was designed to test the effects of overlap on Multilayer Perceptron neural networks. Results are presented for networks trained with the backpropagation algorithm, and with the alternative weight perturbation algorithm. These were carried out using both floating point and quantised weights to examine the combined effects of overlap and weight quantisation. It is shown using examples of classification problems, that the neural network learning is indeed highly tolerent to overlap, such that the effect on performance (i.e. on convergence or generalisation) is negligible for fractional overlaps of up to 30%, and some tolerence is achieved for higher overlaps, before failure eventually occurs. The results of the simulations are followed up by a closer examination of the mechanism of network failure. The last section of the thesis investigates the VLSI implementation of the FDM scheme, and proposes the use of the operational transconductance amplifier (OTA) as a building block for implementation of the FDM circuitry in analogue VLSI. A full custom VLSI design of an OTA is presented, which was designed and fabricated through Eurochip, using HSPICE/Mentor Graphics CAD tools and the Mietec 2.4µ CMOS process. A VLSI architecture for inter-chip FDM is also proposed, using adaptive tuning of the OTA-C filters and oscillators.This forms the basis for a program of further work towards the VLSI realisation of inter-chip FDM, which is outlined in the conclusions chapter

NASA Tech Briefs, December 1990

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences; Life Sciences

NASA Tech Briefs, April 1990

Topics: New Product Ideas; NASA TU Services; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication Technology; Mathematics and Information Sciences

The Fifth NASA Symposium on VLSI Design

The fifth annual NASA Symposium on VLSI Design had 13 sessions including Radiation Effects, Architectures, Mixed Signal, Design Techniques, Fault Testing, Synthesis, Signal Processing, and other Featured Presentations. The symposium provides insights into developments in VLSI and digital systems which can be used to increase data systems performance. The presentations share insights into next generation advances that will serve as a basis for future VLSI design

Technology 2000, volume 1

The purpose of the conference was to increase awareness of existing NASA developed technologies that are available for immediate use in the development of new products and processes, and to lay the groundwork for the effective utilization of emerging technologies. There were sessions on the following: Computer technology and software engineering; Human factors engineering and life sciences; Information and data management; Material sciences; Manufacturing and fabrication technology; Power, energy, and control systems; Robotics; Sensors and measurement technology; Artificial intelligence; Environmental technology; Optics and communications; and Superconductivity