Algorithm and VLSI architecture for real-time 1080p60 video retargeting
EGGH-HPG'12 Proceedings of the Fourth ACM SIGGRAPH / Eurographics conference on High-Performance Graphics
A performance and energy comparison of convolution on GPUs, FPGAs, and multicore processors
ACM Transactions on Architecture and Code Optimization (TACO) - Special Issue on High-Performance Embedded Architectures and Compilers
Accelerators for biologically-inspired attention and recognition
Proceedings of the 50th Annual Design Automation Conference
An FPGA-based accelerator for cortical object classification
DATE '12 Proceedings of the Conference on Design, Automation and Test in Europe
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Biological vision systems use saliency-based visual attention mechanisms to limit higher-level vision processing on the most visually-salient subsets of an input image. Among several computational models that capture the visual-saliency in biological system, an information theoretic AIM(Attention based on Information Maximization) algorithm has been demonstrated to predict human gaze patterns better than other existing models. We present an FPGA based implementation of this computationally intensive AIM algorithm to support embedded vision applications. Our implementation provides performance of processing about 4M pixels/sec for 25 basis functions with a convolution kernel size of 21 by 21 for each of the R, G, and B color-channels, when implemented on a Virtex-6 LX240T. We also provide an optimization aimed at controlling the trade-off between power consumption and latency, and performance comparisons with a GPU implementation.