A case for intelligent disks (IDISKs)
ACM SIGMOD Record
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MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
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ACM Transactions on Database Systems (TODS)
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Searching Multimedia Databases by Content
Searching Multimedia Databases by Content
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ISCA '84 Proceedings of the 11th annual international symposium on Computer architecture
Automatic Evaluation of the Accuracy of Fixed-Point Algorithms
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Parallel Local Histogram Comparison Hardware Architecture for Content-Based Image Retrieval
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Parallel Computing
Performance Analysis of a CBIR System on Shared-Memory Systems and Heterogeneous Clusters
CAMP '05 Proceedings of the Seventh International Workshop on Computer Architecture for Machine Perception
Content-based image retrieval: approaches and trends of the new age
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
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ASP-DAC '03 Proceedings of the 2003 Asia and South Pacific Design Automation Conference
Journal on Image and Video Processing - Color in Image and Video Processing
ARC'07 Proceedings of the 3rd international conference on Reconfigurable computing: architectures, tools and applications
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Because of the growing use of multimedia content over Internet, Content-Based Image Retrieval (CBIR) has recently received a lot of interest. While accurate search techniques based on local image descriptors exist, they suffer from very long execution time. We propose to accelerate CBIR on the RDISK machine, a cluster of FPGA-enhanced hard-drives, that follows the philosophy of smart-disks. Our platform combines coarse and fine grain parallelism thanks to the concurrent use of the cluster nodes and of a programmable logic device. The implementation of the CBIR application on this mixed hardware/software platform follows a strict methodology, that was validated on realistic data-set (image database of more than 30,000 images). This methodology allows us to adapt the original algorithm to suit a hardware implementation, and to select the values of some key design parameters to maximize global performance. Our preliminary results indicate that speed-ups between 120 and 200 could be obtained for a cluster of 32 nodes compared with a software implementation running on a standard desktop PC.