FPGA-Accelerated Pre-Attentive Segmentation in Primary Visual Cortex

Bouganis, Christos-Savvas; Cheung, Peter; Li, Zhaoping

doi:10.1109/fpl.2006.311214

Cited by 8 publications

(12 citation statements)

References 7 publications

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“…Papers that look at accelerating image analysis using FPGAs include Refs. [15][16][17][18][19]. Of these, Refs.…”

Section: Related Workmentioning

confidence: 97%

“…[3], Yadava et al utilize three descriptors to classify objects, while Ref. [15] utilizes a light line projection to determine the three dimensional shape of rotated objects. Papers that look at accelerating image analysis using FPGAs include Refs.…”

Section: Related Workmentioning

confidence: 99%

“…Of these, Refs. [15,19] utilized visual cortex models (although not for image recognition, but rather movement perception in Ref. [19] and attention analysis in Ref.…”

Section: Related Workmentioning

confidence: 99%

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Hardware acceleration of image recognition through a visual cortex model

Rice

Taha

Vutsinas

2008

Optics & Laser Technology

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“…Papers that look at accelerating image analysis using FPGAs include Refs. [15][16][17][18][19]. Of these, Refs.…”

Section: Related Workmentioning

confidence: 97%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Hardware acceleration of image recognition through a visual cortex model

Rice

Taha

Vutsinas

2008

Optics & Laser Technology

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“…At present, we are not aware of any other published studies examining the acceleration of the George and Hawkins model. FPGA acceleration of visual cortex models is seen in [8,12,28]. Torres-Huitzil et al [28] implemented a bioinspired model of visual perception of motion on an FPGA.…”

Section: Modelmentioning

confidence: 98%

“…Their neuron based model with excitatory-inhibitory connectionist processing for 128 × 128 images achieved about a 100 times speedup over a software implementation on a Pentium 4 processor. Bouganis et al [8] examined the FPGA acceleration of a visual attention model in the primary visual cortex (V1). FPGA acceleration showed a 10 times speedup over a 3.2 GHz Pentium 4 processor.…”

Section: Modelmentioning

confidence: 99%

Scaling analysis of a neocortex inspired cognitive model on the Cray XD1

2008

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This paper presents the implementation and scaling of a neocortex inspired cognitive model on a Cray XD1. Both software and reconfigurable logic based FPGA implementations of the model are examined. This model belongs to a new class of biologically inspired cognitive models. Large scale versions of these models have the potential for significantly stronger inference capabilities than current conventional computing systems. These models have large amounts of parallelism and simple computations, thus allowing highly efficient hardware implementations. As a result, hardware-acceleration of these models can produce significant speedups over fully software implementations. Parallel software and hardware-accelerated implementations of such a model are investigated for networks of varying complexity. A scaling analysis of these networks is presented and utilized to estimate the throughput of both hardware-accelerated and software implementations of larger networks that utilize the full resources of the Cray XD1. Our results indicate that hardware-acceleration can provide average throughput gains of 75 times over software-only implementations of the networks we examined on this system.

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