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Upper-bound computation for optimal retargeting in IEEE1687 networks

Ghani Zadegan, Farrokh LU ; Krenz-Baath, Rene and Larsson, Erik LU orcid (2016) 2016 IEEE International Test Conference (ITC) p.1-10
Abstract
IEEE 1687 enables flexible access to on-chip instruments via dynamically reconfigurable networks. Reconfiguration allows reducing instrument access time by keeping only those instruments on the scan-path which are required for each access. To perform reconfiguration and execute commands described in instrument access procedures, scan vectors are generated in a process called retargeting. These vectors are then applied through a number of capture-shift-update (CSU) operations. Generating the optimal set of vectors w.r.t. application time is modeled as an Integer Linear Optimization Problem, which is an NP-hard problem. In the modeling, an IEEE 1687 network is represented as a sequential problem unrolled over a number of time frames, each... (More)
IEEE 1687 enables flexible access to on-chip instruments via dynamically reconfigurable networks. Reconfiguration allows reducing instrument access time by keeping only those instruments on the scan-path which are required for each access. To perform reconfiguration and execute commands described in instrument access procedures, scan vectors are generated in a process called retargeting. These vectors are then applied through a number of capture-shift-update (CSU) operations. Generating the optimal set of vectors w.r.t. application time is modeled as an Integer Linear Optimization Problem, which is an NP-hard problem. In the modeling, an IEEE 1687 network is represented as a sequential problem unrolled over a number of time frames, each frame corresponding to a CSU operation. A key challenge is to find the number of required CSU operations, which should be sufficiently high so that the optimal solution is included in the search space but kept as low as possible to keep the model less complex and thus suitable for large IEEE 1687 networks. In this work, we propose a method to compute an upper-bound on the number of required CSU operations. Through experiments, we show that our method results in a tight upper-bound, is applicable to a large variety of IEEE 1687 network designs, and is able to handle large designs. (Less)
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author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
upper-bound computation, optimal retargeting, On-chip instruments, dynamically reconfigurable networks, instrument access time, instrument access procedures, scan vectors, capture-shift-update operation, CSU operation, vector optimal set generation, integer linear optimization problem, NP-hard problems, sequential problem, time frames, IEEE 1687 network design
host publication
2016 IEEE International Test Conference (ITC)
pages
10 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2016 IEEE International Test Conference (ITC)
conference location
Fort Worth,TX, United States
conference dates
2016-11-15 - 2016-11-17
external identifiers
  • scopus:85013956396
ISBN
978-1-4673-8774-3
978-1-4673-8773-6
DOI
10.1109/TEST.2016.7805838
language
English
LU publication?
yes
id
ca1241bb-0047-400e-8bf4-546cb7f4f195
date added to LUP
2019-04-30 08:40:42
date last changed
2025-01-09 06:58:06
@inproceedings{ca1241bb-0047-400e-8bf4-546cb7f4f195,
  abstract     = {{IEEE 1687 enables flexible access to on-chip instruments via dynamically reconfigurable networks. Reconfiguration allows reducing instrument access time by keeping only those instruments on the scan-path which are required for each access. To perform reconfiguration and execute commands described in instrument access procedures, scan vectors are generated in a process called retargeting. These vectors are then applied through a number of capture-shift-update (CSU) operations. Generating the optimal set of vectors w.r.t. application time is modeled as an Integer Linear Optimization Problem, which is an NP-hard problem. In the modeling, an IEEE 1687 network is represented as a sequential problem unrolled over a number of time frames, each frame corresponding to a CSU operation. A key challenge is to find the number of required CSU operations, which should be sufficiently high so that the optimal solution is included in the search space but kept as low as possible to keep the model less complex and thus suitable for large IEEE 1687 networks. In this work, we propose a method to compute an upper-bound on the number of required CSU operations. Through experiments, we show that our method results in a tight upper-bound, is applicable to a large variety of IEEE 1687 network designs, and is able to handle large designs.}},
  author       = {{Ghani Zadegan, Farrokh and Krenz-Baath, Rene and Larsson, Erik}},
  booktitle    = {{2016 IEEE International Test Conference (ITC)}},
  isbn         = {{978-1-4673-8774-3}},
  keywords     = {{upper-bound computation; optimal retargeting; On-chip instruments; dynamically reconfigurable networks; instrument access time; instrument access procedures; scan vectors; capture-shift-update operation; CSU operation; vector optimal set generation; integer linear optimization problem; NP-hard problems; sequential problem; time frames; IEEE 1687 network design}},
  language     = {{eng}},
  pages        = {{1--10}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Upper-bound computation for optimal retargeting in IEEE1687 networks}},
  url          = {{http://dx.doi.org/10.1109/TEST.2016.7805838}},
  doi          = {{10.1109/TEST.2016.7805838}},
  year         = {{2016}},
}