A FPGA-Based, Granularity-Variable Neuromorphic Processor and Its Application in a MIMO Real-Time Control System
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Summary
The FBGVNP provides a new scheme for building ANNs, which is flexible, highly energy-efficient, and can be applied in many areas, as well as validate the effectiveness of the neuromorphic processor.
- Type
- article
- Published
- 2017-08-23
- Cited by
- 3
- References
- 28
- Access
- Open access
- OpenAlex
- https://openalex.org/W28832522
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:25433569
Keywords
Semiotics, Phenomenon, Beauty, Psychoanalytic theory, Feeling
References
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- Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
- Memory-centric accelerator design for Convolutional Neural Networks
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- DeepFace: Closing the Gap to Human-Level Performance in Face Verification
- Human-level control through deep reinforcement learning
- The SpiNNaker Project
- Large-scale deep learning at Baidu
- Mastering the game of Go with deep neural networks and tree search
- Going Deeper with Embedded FPGA Platform for Convolutional Neural Network
- Deep Learning on FPGAs: Past, Present, and Future
- FPGA based implementation of deep neural networks using on-chip memory only
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