Synergy Exploration in Deploying Convolutional Neural Networks Across Distributed Neuromorphic System

Convolutional Neural Networks (CNNs) are an essential part of distributed neuromorphic systems and require an efficient strategy for deployment. We proposed a synergetic distributed mapping mechanism for CNNs to address the problem of parallel computing and resource optimization of CNNs in neuromorphic systems. A dataset recording soccer positions containing 4000 grayscale images of 105*105 pixels was built. A CNN model was constructed based on it and deployed in the developed neuromorphic system BrainS. As tested in the robotic platform, the model has an accuracy of 95.2% in the test set and can accurately control the movement of the omnidirectional robot. The Root Mean Square Error (NMSE) between BrainS and GPU for all layers during the forward computation of the model was
$2.00 \times {10^{ - 6}}$
. By adjusting the number of guard bits in the computing process, the system's time consumption can be reduced. The proposed method in this paper provides a practical and effective solution for CNN deployment in neuromorphic systems while guaranteeing accuracy.