RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile Vision - implementation -

From Vladimir Koifman's blog, here is Image Sensor Architecture for Continuous Mobile Vision a presentation by Robert LiKamWa on his work. The presentation is at: "RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile Vision" 

 

While the paper is: RedEye: Analog ConvNet Image Sensor Architecture for Continuous Mobile Vision by Robert LiKamWa, Yunhui Hou, Yuan Gao, Mia Polansky, Lin Zhong .

Continuous mobile vision is limited by the inability to efficiently capture image frames and process vision features. This is largely due to the energy burden of analog readout circuitry, data traffic, and intensive computation. To promote efficiency, we shift early vision processing into the analog domain. This results in RedEye, an analog convolutional image sensor that performs layers of a convolutional neural network in the analog domain before quantization. We design RedEye to mitigate analog design complexity, using a modular column-parallel design to promote physical design reuse and algorithmic cyclic reuse. RedEye uses programmable mechanisms to admit noise for tunable energy reduction. Compared to conventional systems, RedEye reports an 85% reduction in sensor energy, 73% reduction in cloudlet-based system energy, and a 45% reduction in computation-based system energy.

 
 The Redee repository is at: https://github.com/JulianYG/redeye_sim that features the following:

RedEye is a vision sensor designed to execute early stages of a deep convolutional neural network (ConvNet) in the analog domain. This repo is a modification of Caffe to train, simulate and visualize analog ConvNet processing under noise vs. energy tradeoffs.

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