| Type of Document |
Dissertation |
| Author |
Lin, Ta-Cheng
|
| Author's Email Address |
tclin1998@hotmail.com |
| URN |
etd-012199-181338 |
| Title |
Digital System Synthesis with Complex Functional Units |
| Degree |
PhD |
| Department |
Electrical and Computer Engineering |
| Advisory Committee |
| Advisor Name |
Title |
| Cyre, Walling R. |
Committee Chair |
| Abbott, A. Lynn |
Committee Member |
| Armstrong, James R. |
Committee Member |
| Gray, Festus Gail |
Committee Member |
| Kafura, Dennis G. |
Committee Member |
|
| Keywords |
- Digital System Synthesis
- Genetic Algorithm
- Communication Buffer
- Partitioned-Bus
Architecture
- Complex Functional Unit
|
| Date of Defense |
1998-12-01 |
| Availability |
unrestricted |
Abstract
The transistor count for todays VLSI technology reaches 40 million transistors on one chip. In
order to successfully design a system with such complexity, new computer-aided design (CAD)
tools are needed. This dissertation shows approaches for coping with the problem of increasing
complexity of VLSI design in three aspects: 1) capturing a higher level of abstraction, 2) using a
new target architecture, and 3) using a new optimization technique.
The advantage of working at a higher level of abstraction is that the number of objects that
designers have to manipulate is reduced so that more complex systems can be delivered in
shorter periods of time. The functions that can be used to capture higher levels of abstraction are
surveyed and categorized into an is-a hierarchy. A partitioned-bus architecture that consists of
complex functional units used to realize complex functions is proposed. The issues of
synthesizing the complex functions to the partitioned-bus architecture are addressed. These
issues are focused on the functional partitioning problem which is a known NP-complete
problem. Algorithms used to optimize several metrics that affect the solution qualities of
functional partitioning are presented. The metrics include communication buffer size, register file
size, system delay, the number of buses, the number of links, and the number of multiplexers.
These metrics are used to form a cost function, which is utilized by the Problem Space Genetic
Partitioning algorithm (PSGP) to search for a good solution. Test cases with known optimal
solutions are used to evaluate the solution qualities that PSGP can attain under run time and
memory space constraints. The experimental results show that PSGP can reach an average about
87% of the optima for two-way partitioning. Another study also shows that PSGP outperforms
the widely used Simulated Annealing algorithm.
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