Date of Award:

5-2010

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Electrical and Computer Engineering

Advisor/Chair:

Dr. Brandon Eames

Abstract

This thesis presents an approach for modeling and generating efficient hardware architectures using constraint programming techniques, targeting field programmable gate arrays (FPGAs). The focus of this thesis is the derivation of optimal or near-optimal schedules for streaming applications from data flow graphs (DFGs). The resulting schedules are then used to facilitate the architecture generation process. Most streaming applications, like digital singal processing (DSP) algorithms, are repetitive in nature: the same computation is performed on different data items. This repetitive nature of streaming applications can be used to expose additional parallelism available across different iterations, by creating multiple instances of the same computation. The replication of the single computation, when applied to high level synthesis (HLS), improves the performance of the design but requires additional area. The amount of additional area required for a replicated graph can be reduced through the use of pipelined functional units and the addition of some extra clock cycles beyond the critical path of the DFG. This thesis discusses the use of a constraint programming (CP)-based scheduler to generate optimal schedules based on designer-provided replication level and critical path relaxation. The scheduler is an integrated part of the design tool, called CHARGER, which analyzes the resulting schedules to allocate memory for storing intermediate data, creates the infrastructure necessary to efficiently execute the application, and finally generates a synthesizable Verilog/VHDL code for the controller. The performance of the architectures derived using the CP-based scheduler is compared with the architectures generated using a force directed scheduling (FDS)-based scheduler for algorithms selected from embedded/multimedia applications. The results show that our CP-based scheduler outperforms the FDS-based scheduler, both in terms of area and efficiency of the generated architectures. The results show average area saving of 39% and average performance improvement of 41%.

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