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Real-Time Multi-Chip Neural Network for Cognitive Systems
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Aalborg :
River Publishers,
2019.
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| Colección: | River Publishers Series in Circuits and Systems Ser.
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Half Title; RIVER PUBLISHERS SERIES IN CIRCUITS AND SYSTEMS; Title Page
- Real-Time Multi-Chip Neural Network for Cognitive Systems; Copyright page; Dedication; Contents; Preface; List of Contributors; List of Figures; List of Tables; List of Abbreviations; Chapter 1
- Introduction; 1.1 A Real-Time Reconfigurable Multi-Chip Architecture for Large-Scale Biophysically Accurate Neuron Simulation; 1.2 The Inferior Olivary Nucleus Cell; 1.2.1 Abstract Model Description; 1.2.2 The ION Cell Design Configuration; 1.2.3 The ION Cell Cluster Controller; 1.3 Multi-Chip Dataflow Architecture
- 1.4 Organization of the BookReferences; Chapter 2
- Multi-Chip Dataflow Architecture for Massive Scale Biophysically Accurate Neuron Simulation; 2.1 Introduction; 2.2 System Design Configuration; 2.2.1 Requirements; 2.2.2 Zero Communication Time: The Optimal Approach; 2.2.3 Localising Communication: How to Speed Up the Common Case; 2.2.4 Network-on-Chips; 2.2.5 Localise Communication between Clusters; 2.2.6 Synchronisation between the Clusters; 2.2.7 Adjustments to the Network to Scale over Multiple FPGAs; 2.2.8 Interfacing the Outside World: Inputs and Outputs
- 2.2.9 Adding Flexibility: Run-Time Configuration2.2.10 Parameters of the System; 2.2.11 Connectivity and Structure Generation; 2.3 System Implementation; 2.3.1 Exploiting Locality: Clusters; 2.3.2 Connecting Clusters: Routers; 2.3.3 Tracking Time: Iteration Controller; 2.3.4 Inputs and Outputs; 2.3.5 The Control Bus for Run-Time Configuration; 2.3.6 Automatic Structure Generation and Connectivity Generation; 2.4 Experimental Results; 2.5 Conclusions; References; Chapter 3
- A Real-Time Hybrid Neuron Network for Highly Parallel Cognitive Systems; 3.1 Introduction
- 3.2 The Calculation Architecture3.2.1 The Physical Cell Overview; 3.2.2 Initialising the Physical Cells; 3.2.3 Axon Hillock + Soma Hardware; 3.2.3.1 Exponent operand schedule; 3.2.3.2 Axon hillock and soma compartment controller; 3.2.4 Dendrite Hardware; 3.2.4.1 Dendrite network operation; 3.2.4.2 Dendrite combine operation; 3.2.4.3 Dendrite compartmental latency; 3.2.5 Calculation Architecture Latency; 3.2.6 Exponent Architecture; 3.3 The Calculation Architecture; 3.3.1 Communication Architecture Overview; 3.3.2 Cluster Controller; 3.3.3 Routing Network; 3.3.3.1 Routing method
- 3.3.3.2 Design specification3.3.4 Interface Bridge; 3.4 Experimental Results; 3.4.1 Evaluation Method; 3.4.1.1 Building a test set; 3.4.1.2 Design simulation; 3.4.1.3 SystemC synthesis; 3.4.1.4 Post-synthesis simulation; 3.4.1.5 VHDL implementation; 3.4.2 Evaluation Results; 3.4.2.1 Accuracy results; 3.4.2.2 Latency results; 3.4.2.3 Resource usage; 3.4.3 Model Configuration; 3.5 Conclusions; References; Chapter 4
- Digital Neuron Cells for Highly Parallel Cognitive Systems; 4.1 Introduction; 4.2 System Design Configuration; 4.2.1 Requirements; 4.2.2 Input and Output; 4.2.3 Parameters