Abstract
A real-world intelligent system consists of three basic modules: environment recognition, prediction (or estimation), and behavior planning. To obtain high quality results in these modules, high speed processing and real time adaptability on a case by case basis are required. In each of the above mentioned modules, many different algorithms and algorithms networks exists and provide various performances on a case by case basis. Thus, a mechanism that for any of the three computational stages selects the best possible algorithm is required. We propose a platform based on the algorithm selection approach to the problem of natural image understanding. This selection mechanism is based on machine learning; a bottom-up algorithm selection from real-world image features and a top-down algorithm selection using information obtained from a high level symbolic world description and algorithm suitability. To accommodate the highspeed processing requirements, the high-frequency of real-time reconfiguration and a low-cost of implementation, we are using present a novel dynamic reconfigurable VLSI processor for real-time adaptation of the algorithm selection. The new architecture includes a fine-grain Digital Reconfigurable Processor, a distributed configuration memory to solve the data transfer bottleneck and an intra-chip packet routing scheme to reduce the size of the configuration memory.
| Original language | English |
|---|---|
| Title of host publication | Proc. of the IADIS Int. Conf. Information Systems Post-Implementation and Change Management 2013, ISPCM 2013, Proc. of the IADIS Int. Conf. Theory and Practice in Modern Computing 2013, TPMC 2013 |
| Pages | 27-34 |
| Number of pages | 8 |
| Publication status | Published - 2013 |
| Externally published | Yes |
| Event | IADIS International Conference Information Systems Post-Implementation and Change Management 2013, ISPCM 2013, IADIS International Conference TPMC 2013, Part of the IADIS Multi Conference on Computer Science and Information Systems 2013, MCCSIS 2013 - Prague, Czech Republic Duration: Jul 22 2013 → Jul 24 2013 |
Other
| Other | IADIS International Conference Information Systems Post-Implementation and Change Management 2013, ISPCM 2013, IADIS International Conference TPMC 2013, Part of the IADIS Multi Conference on Computer Science and Information Systems 2013, MCCSIS 2013 |
|---|---|
| Country | Czech Republic |
| City | Prague |
| Period | 7/22/13 → 7/24/13 |
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Keywords
- Algorithm selection
- Memory reduction
- Natural image processing
- Reconfigurable VLSI
ASJC Scopus subject areas
- Information Systems
Cite this
VLSI platform for real-world intelligent integrated systems based on algorithm selection. / Lukac, Martin; Kameyama, Michitaka; Fujioka, Yoshichika.
Proc. of the IADIS Int. Conf. Information Systems Post-Implementation and Change Management 2013, ISPCM 2013, Proc. of the IADIS Int. Conf. Theory and Practice in Modern Computing 2013, TPMC 2013. 2013. p. 27-34.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - VLSI platform for real-world intelligent integrated systems based on algorithm selection
AU - Lukac, Martin
AU - Kameyama, Michitaka
AU - Fujioka, Yoshichika
PY - 2013
Y1 - 2013
N2 - A real-world intelligent system consists of three basic modules: environment recognition, prediction (or estimation), and behavior planning. To obtain high quality results in these modules, high speed processing and real time adaptability on a case by case basis are required. In each of the above mentioned modules, many different algorithms and algorithms networks exists and provide various performances on a case by case basis. Thus, a mechanism that for any of the three computational stages selects the best possible algorithm is required. We propose a platform based on the algorithm selection approach to the problem of natural image understanding. This selection mechanism is based on machine learning; a bottom-up algorithm selection from real-world image features and a top-down algorithm selection using information obtained from a high level symbolic world description and algorithm suitability. To accommodate the highspeed processing requirements, the high-frequency of real-time reconfiguration and a low-cost of implementation, we are using present a novel dynamic reconfigurable VLSI processor for real-time adaptation of the algorithm selection. The new architecture includes a fine-grain Digital Reconfigurable Processor, a distributed configuration memory to solve the data transfer bottleneck and an intra-chip packet routing scheme to reduce the size of the configuration memory.
AB - A real-world intelligent system consists of three basic modules: environment recognition, prediction (or estimation), and behavior planning. To obtain high quality results in these modules, high speed processing and real time adaptability on a case by case basis are required. In each of the above mentioned modules, many different algorithms and algorithms networks exists and provide various performances on a case by case basis. Thus, a mechanism that for any of the three computational stages selects the best possible algorithm is required. We propose a platform based on the algorithm selection approach to the problem of natural image understanding. This selection mechanism is based on machine learning; a bottom-up algorithm selection from real-world image features and a top-down algorithm selection using information obtained from a high level symbolic world description and algorithm suitability. To accommodate the highspeed processing requirements, the high-frequency of real-time reconfiguration and a low-cost of implementation, we are using present a novel dynamic reconfigurable VLSI processor for real-time adaptation of the algorithm selection. The new architecture includes a fine-grain Digital Reconfigurable Processor, a distributed configuration memory to solve the data transfer bottleneck and an intra-chip packet routing scheme to reduce the size of the configuration memory.
KW - Algorithm selection
KW - Memory reduction
KW - Natural image processing
KW - Reconfigurable VLSI
UR - http://www.scopus.com/inward/record.url?scp=84886903365&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84886903365&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84886903365
SN - 9789728939946
SP - 27
EP - 34
BT - Proc. of the IADIS Int. Conf. Information Systems Post-Implementation and Change Management 2013, ISPCM 2013, Proc. of the IADIS Int. Conf. Theory and Practice in Modern Computing 2013, TPMC 2013
ER -