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Michael P. Kapralos

dblp:97/4953 · also Mike Kapralos · DBLP profile ↗
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3ranked-venue papers
2as first author
1since 2021 · last 2022
0000-0002-8820-9858ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 3 · 2 first-author · 1 since 2021Software engineering, systems software and programming languages · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Reconfigurable computing and FPGAs · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Reconfigurable computing and FPGAs › FPGA resource optimization
FPGA design optimization
0.412019
HOTMeTaL: Hardware Optimization Tool for Memory Table and Logic Conversion · FPGA 2019
Reconfigurable computing and FPGAs
FPGA resource optimization
0.412019
HOTMeTaL: Hardware Optimization Tool for Memory Table and Logic Conversion · FPGA 2019

Methods — techniques the papers use, named apart from their topics

memory table conversion · 0.4logic minimization · 0.4
YearPublicationVenuePosition
2022 Taylor polynomials as an estimator for certain toeplitz matrices
Michael P. Kapralos
J. Supercomput.1
2019 HOTMeTaL: Hardware Optimization Tool for Memory Table and Logic Conversion
abstract
FPGA designs are typically optimized for speed and accuracy, with the amount of available hardware considered after the fact. Balancing speed, accuracy, and hardware utilization is a difficult proposition. In this paper, we demonstrate the Hardware Optimization Tool for Memory Table and Logic Conversion (HOTMeTaL) which helps with this tradeoff by modifying part or the majority of an existing FPGA design to use additional logic resources when memory is at a premium, or force the use of memory when logic is highly utilized; without significantly changing the overall functionality of the design. Using established minimization methods, traditionally used for programmable logic devices (PLDs), we also allow the conversion of groups or individual output bits into minimal logic circuits, which then allows the creation of several functionally identical designs, but allowing more control over the resources used in a design. After introducing the basics of the algorithm, we demonstrate the utility of this process with direct comparisons to examples from previous works. It is demonstrated that in some cases, our system will reduce the amount of logic (slices) while simultaneously removing the
Michael P. Kapralos, John A. Chandy
FPGA1
2008 Layout-Aware, IR-Drop Tolerant Transition Fault Pattern Generation
abstract
Market and customer demands have continued to push the limits of CMOS performance. At-speed test has become a common method to ensure these high performance chips are being shipped to the customers fault-free. However, at-speed tests have been known to create higher-than-average switching activity, which normally is not accounted for in the design of the power supply network. This potentially creates conditions for additional delay in the chip; causing it to fail during test. In this paper, we propose a pattern compaction technique that considers the layout and gate distribution when generating transition delay fault patterns. The technique focuses on evenly distributing switching activity generated by the patterns across the layout rather than allowing high switching activity to occur in a small area in the chip that could occur with conventional delay fault pattern generation. Due to the relationship between switching activity and IR-drop, the reduction of switching will prevent large IR-drop in high demand regions while still allowing a suitable amount of switching to occur elsewhere on the chip to prevent fault coverage loss. This even distribution of switching on the chip will also result in avoiding hot-spots.
Jeremy Lee, Sumit Narayan, Michael P. Kapralos, Mark Tehranipoor
DATE3