Bio-inspired error-tolerant and energy-efficient signal processing

In many applications at the sensory edge, such as security and environmental sensing, reliable sensor nodes must operate for extended time periods on battery supplies. To meet this constraint, energy-efficient systems have been developed through different technologies. The primary and the most effective approach has been technology scaling. Another emerging technique is to operate circuits in the subthreshold region as some of the applications such as environmental sensing do not require high throughput. However, both techniques lead to large process, voltage and temperature variations and therefore jeopardize system reliability. In order to achieve both energy savings and system reliability, we take inspirations from biological methods, such as population-coding, and apply these methods to a canonical problem of non-coherent (unknown phase) frequency estimation. Energy efficiency is achieved using low cost, overlapping band-pass filters rather than conventional non-overlapping band-pass filters. Energy savings are also achieved by operating the hardware at a voltage lower than the nominal voltage (voltage overscaling), which leads to hardware timing errors. In the presence of these hardware errors, signal statistics are generated from overlapping band-pass filters with frequency redundancy. Robust techniques, such as median estimation and algorithmic noise-tolerance, are applied to filter outputs to achieve error-tolerance. Energy/performance trade-offs are further explored by altering the supply voltage. Simulation results show that the root-mean-squared-error of the bio-inspired method can be reduced by an order of magnitude relative to that of the conventional architecture while achieving an energy consumption reduction of 78% relative to the conventional method which is under hardware-error-free operations at nominal supply voltage.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Zhang, Gong

Contributors dc:contributor

• Shanbhag, Naresh R.
• Jones, Douglas L.

• Bio-inspired
• Population coding
• Energy-efficient
• Error-tolerant
• Voltage over scaling

Statement dc:rights

• Copyright 2012 Gong Zhang

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/42135

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/42135