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As a large-scale commercial spiking-based neuromorphic computing platform, IBM TrueNorth processor received tremendous attentions in society. However, one of the known issues in TrueNorth design is the limited precision of synaptic weights. The current workaround is running multiple neural network copies in which the average value of each synaptic weight is close to that in the original network. We...
Road scene understanding and semantic segmentation is an on-going issue for computer vision. A precise segmentation can help a machine learning model understand the real world more accurately. In addition, a well-designed efficient model can be used on source limited devices. The authors aim to implement an efficient high-level, scene understanding model in an embedded device with finite power and...
Autonomous driving can effectively reduce traffic congestion and road accidents. Therefore, it is necessary to implement an efficient high-level, scene understanding model in an embedded device with limited power and sources. Toward this goal, we propose ApesNet, an efficient pixel-wise segmentation network, which understands road scenes in real-time, and has achieved promising accuracy. The key findings...
As the first large-scale commercial spiking-based neuromorphic computing platform, IBM TrueNorth chip received tremendous attentions in society. However, one of the known issues in TrueNorth design is the limited precision of synaptic weights, each of which can be selected from only four integers. The current workaround is running multiple neural network copies of which the average value of each synaptic...
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