Power estimation model based on grouping components in field-programmable gate array circuit

Najoua, Chalbi; Boubaker, Mohamed; Hedi, Bedoui Mohamed

doi:10.1049/iet-cds.2011.0367

Cited by 1 publication

(2 citation statements)

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“…It is represented by a curve marked by stars. The curve marked by pluses shows the approximated analytical dynamic power model [24] as a function of these parameters: power of one neuron, network topology, area (number of slices) and time whereas the last one shows the error between the two curves. The approximated model is obtained by the polynomial approximation method based on the Levenberg-Marquard algorithm [31].…”

Section: Implementation Results and Discussionmentioning

confidence: 99%

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Implementation of a low‐power LVQ architecture on FPGA

Najoua

Boubaker

Bedoui

2017

IET Circuits, Devices & Syst

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This study presents an architecture-optimising methodology for embedding an learning vector quantization (LVQ) neural network on an field programmable gate array (FPGA) device. The embedded architecture contains both learning and decision circuitry and is optimised towards the lowest power/energy consumption. The low-power/energy architecture is obtained through the selection of the best one amongst a number of architectures produced by FPGA software design tools that combine power, area and the ergonomic utilisation of internal FPGA resources. A complete characterisation of power at the architectural level was carried out using the Xpower tool. An analytical power model was determined by the following parameters: area, delay and LVQ topology. Concerning the authors' architecture, there is a 28% gain in the area design. Moreover, it consumes 8% power in the nanoboard 3000 compared with the other ones.

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Section: Implementation Results and Discussionmentioning

confidence: 99%

“…Based on our expertise in estimating and optimising the power consumption at a high level [23, 24], while bringing an adaptation of the LVQ parameters, we propose an LVQ implementation approach where power consumption is taken into account at a high level to save the design time.…”

Section: Introduction and Related Workmentioning

confidence: 99%