Date of Graduation
Doctor of Philosophy in Engineering (PhD)
Computer Science & Computer Engineering
James P. Parkerson
Second Committee Member
Third Committee Member
Application Specific Integrated Circuit (ASIC), Asynchronous, Neural Networks
Artificial intelligence (AI) has experienced a tremendous surge in recent years, resulting in high demand for a wide array of implementations of algorithms in the field. With the rise of Internet-of-Things devices, the need for artificial intelligence algorithms implemented in hardware with tight design restrictions has become even more prevalent. In terms of low power and area, ASIC implementations have the best case. However, these implementations suffer from high non-recurring engineering costs, long time-to-market, and a complete lack of flexibility, which significantly hurts their appeal in an environment where time-to-market is so critical. The time-to-market gap can be shortened through the use of reconfigurable solutions, such as FPGAs, but these come with high cost per unit and significant power and area deficiencies over their ASIC counterparts. To bridge these gaps, this dissertation work develops two methodologies to improve the usability of ASIC implementations of neural networks in these applications.
The first method demonstrates a method for substantial reductions in design time for asynchronous implementations of a set of AI algorithms known as Recurrent Neural Networks (RNN) by analyzing the possible architectures and implementing a library of generic or easily altered components that can be used to quickly implement a chosen RNN architecture. A tapeout of this method was completed using as few as 112 hours of labor by the designer from RNN selection to a DRC/LVS clean chip layout ready for fabrication.
The second method develops a flow to implement a set of RNNs in a single reconfigurable ASIC, offering a middle ground between fully reconfigurable solutions and completely application-specific implementations. This reconfigurable design is capable of representing thousands of possible RNN configurations in a single IC. A tapeout of this design was also completed, with both tapeouts using the TSMC 65nm bulk CMOS process.
Nelson, S. (2021). Low-Power and Reconfigurable Asynchronous ASIC Design Implementing Recurrent Neural Networks. Graduate Theses and Dissertations Retrieved from https://scholarworks.uark.edu/etd/4033