ArticleFrontiers in neuroscience2019
REMODEL: Rethinking Deep CNN Models to Detect and Count on a NeuroSynaptic System.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Spiking Neural Networks in Imaging: A Review and Case Study.Sensors (Basel, Switzerland) · 2025Review
- Exploring Types of Photonic Neural Networks for Imaging and Computing-A Review.Nanomaterials (Basel, Switzerland) · 2024Review
- End-to-End Implementation of Various Hybrid Neural Networks on a Cross-Paradigm Neuromorphic Chip.Frontiers in neuroscience · 2021Article
- Prediction Model of Aryl Hydrocarbon Receptor Activation by a Novel QSAR Approach, DeepSnap-Deep Learning.Molecules (Basel, Switzerland) · 2020Article
- HFNet: A CNN Architecture Co-designed for Neuromorphic Hardware With a Crossbar Array of Synapses.Frontiers in neuroscience · 2020Article
- Convolutional-Neural Network-Based Image Crowd Counting: Review, Categorization, Analysis, and Performance Evaluation.Sensors (Basel, Switzerland) · 2019Review
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5 authors.
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Abstract
In this work, we perform analysis of detection and counting of cars using a low-power IBM TrueNorth Neurosynaptic System. For our evaluation we looked at a publicly-available dataset that has overhead imagery of cars with context present in the image. The trained neural network for image analysis was deployed on the NS16e system using IBM's EEDN training framework. Through multiple experiments we identify the architectural bottlenecks present in TrueNorth system that does not let us deploy large neural network structures. Following these experiments we propose changes to CNN model to circumvent these architectural bottlenecks. The results of these evaluations have been compared with caffe-based implementations of standard neural networks that were deployed on a Titan-X GPU. Results showed that TrueNorth can detect cars from the dataset with 97.60% accuracy and can be used to accurately count the number of cars in the image with 69.04% accuracy. The car detection accuracy and car count (-/+ 2 error margin) accuracy are comparable to high-precision neural networks like AlexNet, GoogLeNet, and ResCeption, but show a manifold improvement in power consumption.
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