David Sheldon
University of California, Riverside
11 Papers
155 Citations
David Sheldon is an academic researcher from University of California, Riverside. The author has contributed to research in topics: Field-programmable gate array & Design of experiments. The author has an hindex of 6, co-authored 11 publications. Previous affiliations of David Sheldon include University of California, Berkeley.
Chat about Author
Papers
Application-specific customization of parameterized FPGA soft-core processors
David Sheldon,Rakesh Kumar,Roman Lysecky,Frank Vahid,Dean M. Tullsen +4 more
- 05 Nov 2006
TL;DR: Two approaches are considered, one using a traditional CAD approach that does an initial characterization using synthesis to create an abstract problem model and then explores the solution space using a knapsack algorithm, and the other using a synthesis-in-the-loop exploration approach that improved speedups by an average of 20% when size constraints were tight.
Interactive presentation: Soft-core processor customization using the design of experiments paradigm
David Sheldon,Frank Vahid,Stefano Lonardi +2 more
- 16 Apr 2007
TL;DR: In this paper, the authors focus on the problem of tuning a parameterized soft-core microprocessor to achieve the best performance on a particular application, subject to size constraints, using a well-established statistical paradigm called Design of Experiments (DoE).
37
Conjoining soft-core FPGA processors
David Sheldon,Rakesh Kumar,Frank Vahid,Dean M. Tullsen,Roman Lysecky +4 more
- 05 Nov 2006
TL;DR: An efficient dynamic-programming-based exploration method is introduced to find the best custom instantiation of hardware units, considering both standalone and conjoined options, for soft-core processors.
Making good points: application-specific pareto-point generation for design space exploration using statistical methods
David Sheldon,Frank Vahid +1 more
- 22 Feb 2009
TL;DR: An algorithm for finding Pareto points is introduced, based on statistically rigorous methods derived from the Design of Experiments paradigm and extended for the purpose of finding Paredto points, without requiring designer knowledge of parameter interdependencies.
16
Dynamic tuning of configurable architectures: the AWW online algorithm
Chen Huang,David Sheldon,Frank Vahid +2 more
- 19 Oct 2008
TL;DR: The adaptive weighted window (AWW) algorithm is introduced, and several other algorithms are compared, including algorithms previously developed by the online algorithm community, to show that AWW outperforms the other algorithms, and is robust across three datasets and across three categories of application sequences too.