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David J. Lilja
Researcher at University of Minnesota
Publications - 284
Citations - 7389
David J. Lilja is an academic researcher from University of Minnesota. The author has contributed to research in topics: Stochastic computing & Cache. The author has an hindex of 39, co-authored 283 publications receiving 6836 citations. Previous affiliations of David J. Lilja include University of Western Australia & Sun Microsystems.
Papers
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Journal ArticleDOI
MinneSPEC: A New SPEC Benchmark Workload for Simulation-Based Computer Architecture Research
A.J. KleinOsowski,David J. Lilja +1 more
TL;DR: The MinneSPEC inputset for the SPEC CPU 2000 benchmark suite is developed to facilitate efficient simulations with a range of benchmarkprograms and it is found that for some programs, the Minne SPECprofiles match the SPEC reference dataset program behavior very closely; for other programs, however, theMinneSPEC inputs produce significantly different programbehavior.
Journal ArticleDOI
An Architecture for Fault-Tolerant Computation with Stochastic Logic
TL;DR: The concept of stochastic logic is applied to a reconfigurable architecture that implements processing operations on a datapath and it is found to be much more tolerant of soft errors than conventional hardware implementations.
MonographDOI
Measuring computer performance : A practitioner's guide
TL;DR: Measuring Computer Performance as mentioned in this paper describes the fundamental techniques used in analyzing and understanding the performance of computer systems and provides a detailed explanation of the key statistical tools needed to interpret measured performance data, and describes the general "design of experiments" technique, and shows how the maximum amount of information can be obtained for the minimum effort.
Journal ArticleDOI
Data prefetch mechanisms
TL;DR: To be effective, prefetching must be implemented in such a way that prefetches are timely, useful, and introduce little overhead, and secondary effects such as cache pollution and increased memory bandwidth requirements must be taken into consideration.
Journal ArticleDOI
Computation on Stochastic Bit Streams Digital Image Processing Case Studies
TL;DR: This paper introduces new SCEs based on finite-state machines based on FSMs for the task of digital image processing and compares the error tolerance, hardware area, and latency of stochastic implementations to those of conventional deterministic implementations using binary radix encoding.