Multiclass Object Recognition with Sparse, Localized Features
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields
International Journal of Computer Vision
A dynamically configurable coprocessor for convolutional neural networks
Proceedings of the 37th annual international symposium on Computer architecture
An FPGA Implementation of Information Theoretic Visual-Saliency System and Its Optimization
FCCM '11 Proceedings of the 2011 IEEE 19th Annual International Symposium on Field-Programmable Custom Computing Machines
Profiling Heterogeneous Multi-GPU Systems to Accelerate Cortically Inspired Learning Algorithms
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
Accelerators for biologically-inspired attention and recognition
Proceedings of the 50th Annual Design Automation Conference
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Recently significant advances have been achieved in understanding the visual information processing in the human brain. The focus of this work is on the design of an architecture to support HMAX, a widely accepted model of the human visual pathway. The computationally intensive nature of HMAX and wide applicability in real-time visual analysis application makes the design of hardware accelerators a key necessity. In this work, we propose a configurable accelerator mapped efficiently on a FPGA to realize real-time feature extraction for vision-based classification algorithms. Our innovations include the efficient mapping of the proposed architecture on the FPGA as well as the design of an efficient memory structure. Our evaluation shows that the proposed approach is significantly faster than other contemporary solutions on different platforms.