Path planning by spike propagation

Koul, Shashikant; Horiuchi, Timothy K.

doi:10.1109/biocas.2015.7348409

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Cited by 10 publications

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“…This platform's low‐power consumption and programmable, spiking neurons offer an ideal hardware system on which to implement robotic navigation applications. Prior work investigating path planning algorithms on neuromorphic hardware has used spiking VLSI neurons [2, 3], where an array which fits a map of 4–100 neurons or nodes is used to propagate spikes throughout the map. An associated microcontroller stores the address event representation (AER) timing information of the silicon neurons’ spike activity.…”

Section: Introductionmentioning

confidence: 99%

Spike propagation path planning on IBM TrueNorth neurosynaptic system

Fischl¹,

Fair²,

Tsai³

et al. 2017

Electron. lett.

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The authors report on the implementation of a path planning algorithm on the TrueNorth neurosynaptic system. Their implementation exploits processing in the temporal domain within the architectural constraints of the TrueNorth chip to deduce the optimal path. The optimal path is computed on the TrueNorth chip for grid maps with dimensions as large as 173 × 168 nodes consuming ∼70 mW at an operating voltage of 0.8 V.

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Section: Introductionmentioning

confidence: 99%