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HDL Descriptions of Artificial Neuron Activation Functions

Srinivasan, Vikram
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1121113992

Abstract Details

2005, MS, University of Cincinnati, Engineering : Electrical Engineering.
A significant development in recent times has been the remarkable ability to create artificial intelligent systems which are capable of thinking and making decisions independently. To a large extent, this has been a fruit of modeling systems based on existing prototypes like the human body. Conforming to this definition, artificial neural networks mimic biological neural networks in their structure and capacity. Such designs have been physically fabricated in silicon with the advent of Very Large Scale Integration (VLSI) and there is an increasing demand to make larger networks on a smaller scale, by reducing component sizes. In keeping with this trend, Hardware Description Languages offer a powerful medium for designers to express their ideas in code-style; a far less complicated option vis-à-vis graphic design tools like MAGIC. Although automated synthesis of HDL codes has not advanced significantly, there is no doubt that such design codes are easy to develop, understand, simulate and optimize. Almost all commercial practices adopt the HDL style of design today and we are not too far from coming up with complementary synthesis technologies. In our efforts to fabricate entire artificial neural networks as systems on chips, we must first develop component models in HDL. This thesis attempts to describe commonly used neuronal activation functions like the Linear Threshold, Sigmoid and Gaussian functions in Verilog-AMS. This is a popular HDL with Mixed Signal extensions to analog and digital design. These models have been described in their structural and behavioral sense. The results have been compared to existing SPICE simulations to verify accuracy. Disparities between simulation results in certain models can be observed upon comparison, which can be attributed to a difference in MOS transistor descriptions between SPICE and Verilog models. Although we are yet to fully tap the potential of a convenient designing language like Verilog-AMS, a library of neurons can enable us to develop HDL codes for entire networks, which will ultimately be efficient, convenient and cost-effective for automatic synthesis when we optimize our designs completely with the powerful features this language has to offer.
Dr. Carla Purdy (Advisor)
159 p.
Verilog-AMS; Neuron; Activation Functions; HDL; Analog design

Recommended Citations

Citations

  • Srinivasan, V. (2005). HDL Descriptions of Artificial Neuron Activation Functions [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1121113992

    APA Style (7th edition)

  • Srinivasan, Vikram. HDL Descriptions of Artificial Neuron Activation Functions. 2005. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1121113992.

    MLA Style (8th edition)

  • Srinivasan, Vikram. "HDL Descriptions of Artificial Neuron Activation Functions." Master's thesis, University of Cincinnati, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1121113992

    Chicago Manual of Style (17th edition)

ucin1121113992
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© 2005, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.