Low-power smart imagers for vision-enabled wireless sensor networks and a case study
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1 Low-power smart imagers for vision-enabled wireless sensor networks and a case study J. Fernández-Berni, R. Carmona-Galán, Á. Rodríguez-Vázquez Institute of Microelectronics of Seville (IMSE-CNM), CSIC - Universidad de Sevilla (Spain) First ACM/IEEE International Workshop On Architecture of Smart Camera Clermont Ferrand, France April 5-6, 2012
2 WASC April 2012, Clermont-Ferrand (France) Low-level image processing INSTRUCTION FLOW, COMPUTATIONAL LOAD Sobel Operators - Potential parallel operation - Moderate accuracy required
3 WASC April 2012, Clermont-Ferrand (France) Conventional approach Analytic issues are mostly software issues Brute force pattern matching used by many system developers Extremely inefficient in terms of speed and power Imager Digital Signal Processor ADC Low-level tasks Mid-level tasks High-level tasks F F f f ARRAY OF SENSORS Information Flow: F >> f > f
4 WASC April 2012, Clermont-Ferrand (France) Focal-plane array computing Content-aware sensing-processing Progressive extraction of relevant information Parallel and distributed processing Distributed memory Smart Imager Digital Signal Processor f ADC f Mid-level tasks f High-level tasks ARRAY OF SENSOR PROCESSORS Information Flow: f > f
5 Focal-plane array computing Sensor Memory PER PIXEL: Mixed signal processor WASC April 2012, Clermont-Ferrand (France) Optical sensing Neighbor Connectivity Local Processing Local Memory Single Instruction (SIMD) Architecture Multiple Data
6 Focal-plane array computing Several FLIP-Q generation of chips designed With fully programmable features Covering a large functional targets Image-to-Decision variety at of >1,000F/s with 60nW per pixel Spatio-temporal filtering with 22nJ/cycle Content-aware HDR acquisition with >145dB intra-frame DR Etc. WASC April 2012, Clermont-Ferrand (France)
7 WASC April 2012, Clermont-Ferrand (France) FLIP-Q: floorplan J. Fernández Berni, R. Carmona Galán and L. Carranza González, FLIP-Q: A QCIF Resolution Focal-Plane Array for Low-Power Image Processing, in IEEE J. Solid-State Circuits, vol. 46, no. 3, pp , March 2011
8 WASC April 2012, Clermont-Ferrand (France) FLIP-Q: elementary processing cell Reset transistor n-well/p-substrate photodiode Electronic global shutter Programmable block-wise image filtering and averaging Programmable block-wise image energy computation Readout circuitry
9 WASC April 2012, Clermont-Ferrand (France) Physical design Crucial aspect affecting the total area, the fill factor and the pixel pitch 29µm The electrical design must be realized bearing in mind the subsequent physical design Relevant issues: Metal layers available Full-custom routing Make the most of the design rules 34µm
10 WASC April 2012, Clermont-Ferrand (France) Physical design 60% +2% per additional µm in the elementary cell
11 FLIP-Q: A prototype smart imager WASC April 2012, Clermont-Ferrand (France)
12 WASC April 2012, Clermont-Ferrand (France) FLIP-Q: on-chip early vision Programmable Gaussian filtering error ideal chip
13 WASC April 2012, Clermont-Ferrand (France) FLIP-Q: on-chip early vision Fully-programmable multi-resolution scene representation On-chip images
14 WASC April 2012, Clermont-Ferrand (France) FLIP-Q: on-chip early vision Image pre-distortion for reduced kernel filtering Original kernels Reduced kernels
15 WASC April 2012, Clermont-Ferrand (France) Wi-FLIP: a vision-enabled WSN node Smart Imager Digital Signal Processor f ADC f Mid-level tasks f High-level tasks ARRAY OF SENSOR PROCESSORS Information Flow: f > f
16 WASC April 2012, Clermont-Ferrand (France) Wi-FLIP: a vision-enabled WSN node Imote2 (MEMSIC Inc.)
17 Wi-FLIP: a vision-enabled WSN node WASC April 2012, Clermont-Ferrand (France)
18 Wi-FLIP: a vision-enabled WSN node WASC April 2012, Clermont-Ferrand (France)
19 WASC April 2012, Clermont-Ferrand (France) Wi-FLIP: a vision-enabled WSN node DoG-based edge detection
20 WASC April 2012, Clermont-Ferrand (France) Wi-FLIP: a vision-enabled WSN node Very low throughput due to slow GPIO ports and TinyOS latency
21 27 Case study: early detection of forest fires High economic cost Short maintenance cycles Coarse grain coverage Exact location must be inferred
22 28 Case study: early detection of forest fires Vision-enabled Wireless Sensor Network ADVANTAGES Robustness Scalability Reliability Better temporal resolution Simpler smoke location DRAWBACKS Ultra low power consumption required
23 29 Case study: early detection of forest fires Reconfigurable focal plane A power-efficient vision algorithm Multiresolution forscene smoke representation detection Candidate regions Clustering ratio Growth rate SMOKE! Propagation speed
24 30 Case study: early detection of forest fires Preliminary field tests Original sequence Motion detector Our algorithm
25 31 Case study: early detection of forest fires On-site smoke detection with Eye-RIS v1.2
26 32 Case study: early detection of forest fires Field tests with Wi-FLIP
27 33 Case study: early detection of forest fires Prescribed burning of a 95m x 20m shrub plot Wi-FLIP monitored all the activity for over two hours No false alarm triggered Successful smoke detection for two of the three vegetation areas explored Thin smoke generated from a very sparse vegetation area was not detected
28 34 CONCLUSIONS Early vision tasks represent a considerably heavy computational load. SIMD-based massively parallel mixed-signal processing takes advantage of their intrinsic characteristics to achieve high power efficiency and computational power. FLIP-Q: A prototype vision chip tailored for ultra low-power applications. Very competitive in the state of the art. Wi-FLIP: A vision-enabled Wireless Sensor Network node supported by Imote2. Current drawback: low throughput. Case study: Early detection of forest fires, with very good results in terms of reliability.
29 35 Thank you very much for your attention Publication Date: May 31, 2012
30 36 Acknowledgments This work is financially supported by Andalusian Regional Government, through project 2006-TIC-2352, the Spanish Ministry of Economy and Competitiveness, through projects TEC and IPT , both co-funded by the EU-ERDF and by the Office of Naval Research (USA), through grant N
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