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[null,"1.","Record Nr.",null,"UNISA996495560503316"]
[null,null,"Titolo",null,"Near-sensor and in-sensor computing / / Yang Chai and Fuyou Liao, editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-11506-6"]
[null,null,"Descrizione fisica",null,"1 online resource (237 pages)"]
[null,null,"Disciplina",null,"006.22"]
[null,null,"Soggetti",null,"Cooperating objects (Computer systems)"]
[{"$nulls":4},"Sensor networks"]
[{"$nulls":4},"Neural networks (Computer science)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Contents -- Chapter 1: Neuromorphic Computing Based on Memristor Dynamics -- 1.1 Introduction -- 1.2 Artificial Synapses -- 1.2.1 Long-Term Plasticity -- 1.2.2 Short-Term Plasticity -- 1.3 Artificial Neuron -- 1.3.1 H-H Neuron -- 1.3.2 LIF Neurons -- 1.3.3 Oscillation Neuron -- 1.3.4 Artificial Dendrites -- 1.4 Memristor-Based Neuromorphic Computing Systems -- 1.4.1 Memristive Reservoir Computing Systems -- 1.4.2 Memristor-Based Coupled Oscillator Network -- 1.4.3 Memristor-Based Continuous Attractor Neural Network -- 1.4.4 Memristive Spiking Neural Network -- 1.4.5 Memristor-Based Chaotic Computing -- 1.5 Conclusions and Outlook -- References -- Chapter 2: Short-Term Plasticity in 2D Materials for Neuromorphic Computing -- 2.1 Introduction -- 2.2 Sound Localization via 2D Synaptic Devices -- 2.2.1 Short-Term Plasticity -- 2.2.2 Joule Heating for STP -- 2.2.3 Tunable STP in 2D Material-Based Synaptic Devices -- 2.2.4 Synaptic Computation for Sound Localization -- 2.2.5 2D Materials Engineering for Optimized STP -- 2.3 In-Sensor Reservoir Computing for Language Learning via 2D Memristors -- 2.3.1 Recurrent Neural Networks and Reservoir Computing -- 2.3.2 SnS Memristor for Optoelectronic RC -- 2.3.3 Spatiotemporal Signal Processing in a Circuit Based on SnS Memristors -- 2.3.4 Optoelectronic RC for the Learning of the Korean Language -- 2.3.5 Inference of Korean Sentences via Optoelectronic RC -- 2.4 Conclusion and Outlook -- References -- Chapter 3: Bioinspired In-Sensor "]
[{"$nulls":4},"Computing Devices for Visual Adaptation -- 3.1 Introduction -- 3.2 The Visual Adaptation of the Retina -- 3.3 In-Sensor Visual Adaptation Based on Emerging Devices -- 3.3.1 Two-Terminal Optoelectronic Devices -- 3.3.2 Three-Terminal Phototransistors -- 3.3.3 Optoelectronic Circuits -- 3.4 Conclusion and Future Prospects -- References."]
[{"$nulls":4},"Chapter 4: Neuromorphic Vision Based on van der Waals Heterostructure Materials -- 4.1 Introduction -- 4.2 Retinomorphic Sensor -- 4.2.1 Mimicking Retinal Cells with Vertical Heterostructure Devices -- 4.2.2 Reconfigurable Retinomorphic Vision Sensor -- 4.3 Neuromorphic Vision System -- 4.3.1 The Architecture of Neuromorphic Vision System -- 4.3.2 The Function of Neuromorphic Vision System -- 4.4 Conclusion -- References -- Chapter 5: Neuromorphic Vision Chip -- 5.1 Introduction -- 5.2 Vision Chip Architectures -- 5.2.1 Early Frame-Driven Vision Chips -- 5.2.2 Dynamically Reconfigurable Vision Chips -- 5.2.2.1 Architecture Design of Dynamically Reconfigurable Vision Chips -- 5.2.2.2 Architecture Characteristics of Dynamically Reconfigurable Vision Chips -- 5.2.3 Convolutional Neural Network-Oriented Vision Chip Architecture -- 5.2.3.1 Convolutional Neural Network-Oriented Vision Chip Architecture Design -- 5.2.3.2 Optimization Strategy -- 5.2.3.3 Development Status of the Convolutional Neural Network Accelerator -- 5.2.4 Programmable Parallel Vision Chip -- 5.2.4.1 Design of Programmable Parallel Architecture -- 5.2.4.2 Features of Programmable Parallel Architecture -- 5.3 Visual Tasks and Software on Vision Chips -- 5.3.1 Categories of Various Visual Tasks and Model Selection -- 5.3.2 Network Architecture Determination -- 5.3.2.1 On-Chip Memory -- 5.3.2.2 On-Chip Computing Resources -- 5.3.2.3 Specialized Hardware Units -- 5.3.2.4 Other Constraints -- 5.3.3 Model Compression on Vision Chips -- 5.3.3.1 Lightweight Model Pruning -- 5.3.3.1.1 Fine-Grained Pruning and Structured Pruning -- 5.3.3.1.2 Regularization-Based Structured Pruning -- 5.3.3.2 Network Quantization -- 5.3.3.2.1 Post-training Quantization and Quantization-Aware Training -- 5.3.3.3 Precision Alignment After Quantization."]
[{"$nulls":4},"5.3.3.3.1 Model Accuracy Verification on a Vision Chip Simulator -- 5.3.3.3.2 Solutions for Accuracy Mismatch -- 5.3.4 Model Mapping on Vision Chips -- 5.3.4.1 Dataflow on Vision Chips -- 5.3.4.1.1 Integer Inference Dataflow -- 5.3.4.1.2 Overflow Handling and Bit-Shifting -- 5.3.4.2 Manually Designed Operator Library -- 5.3.4.2.1 Interface Design of the Library -- 5.3.4.2.2 Performance Optimization of the Library -- 5.3.4.2.3 Mapping of the Model to the Library -- 5.4 Conclusion -- References -- Chapter 6: Collision Avoidance Systems and Emerging Bio-inspired Sensors for Autonomous Vehicles -- 6.1 Introduction -- 6.2 Sensors for CAS -- 6.2.1 Light Detection and Ranging (LiDAR) -- 6.2.2 Radio Detection and Ranging (Radar) -- 6.2.3 Ultrasonic Sensors -- 6.2.4 Image Sensors -- 6.3 Bio-inspired Sensors -- 6.4 Sensor Fusion Technology -- 6.5 Summary -- References -- Chapter 7: Emerging Devices for Sensing-Memory-Computing Applications -- 7.1 Introduction -- 7.1.1 History and Emerging Technology -- 7.1.2 The Challenge for Sensing-Memory-Computing -- 7.1.2.1 Material System and Device Structure -- 7.1.2.2 Array Integration -- 7.2 Metal Oxide-Based Sensing-Memory-Computing Device -- 7.2.1 Research Background and Status -- 7.2.2 Several Device Types Based on Metal Oxide Film -- 7.2.2.1 Single Two-Terminal Memristor -- 7.2.2.2 Memristor Array -- 7.2.3 Issues and Challenges -- 7.2.4 Conclusion and Outlook -- 7.3 Two-Dimensional Sensing-Memory-Computing Device -- 7.3.1 Research "]
[{"$nulls":4},"Background and Status -- 7.3.1.1 Two-Dimensional Materials -- 7.3.1.2 Library of 2D Materials and Their Heterostructures -- 7.3.1.3 Preparation of 2D Materials and Their Heterostructures -- 7.3.2 Several Device Types Based on Two-Dimensional Film -- 7.3.2.1 Charge-Based Device -- 7.3.2.2 Resistive Switching Device -- 7.3.3 Issues and Challenges -- 7.3.4 Conclusion and Outlook."]
[{"$nulls":4},"7.4 Organic Sensing-Memory-Computing Device -- 7.4.1 Research Background and Status -- 7.4.2 Several Device Types Based on Organic Film -- 7.4.2.1 Tactile Sensing-Memory-Computing Device -- 7.4.2.2 Visual Sensing-Memory-Computing Device -- 7.4.2.3 Olfaction Sensing-Memory-Computing Device -- 7.4.2.4 Auditory Sensing-Memory-Computing Device -- 7.4.3 Issues and Challenges -- 7.4.4 Conclusion and Outlook -- 7.5 Phase Change Sensing-Memory-Computing Device -- 7.5.1 Research Background and Status -- 7.5.2 Several Device Types Based on Phase Change Material -- 7.5.3 Issues and Challenges -- 7.5.4 Conclusion and Outlook -- 7.6 Ferroelectric Sensing-Memory-Computing Device -- 7.6.1 Research Background and Status -- 7.6.2 Several Device Types Based on Ferroelectric Material -- 7.6.2.1 Piezoelectric Device -- 7.6.2.2 Logic Device -- 7.6.2.3 Storage Device -- 7.6.2.4 Optical Device -- 7.6.3 Issues and Challenges -- 7.6.4 Conclusion and Outlook -- 7.7 Conclusion -- References -- Chapter 8: Neural Computing with Photonic Media -- 8.1 Introduction -- 8.2 Nanophotonic Medium for Neural Computing -- 8.2.1 Implementation -- 8.2.2 Training Process -- 8.3 Fabrication Constraints -- 8.3.1 B-Splines -- 8.4 Neuromorphic Metasurfaces -- 8.4.1 System Setup -- 8.4.2 Training Process -- 8.4.3 Results -- 8.5 Conclusion -- References -- Chapter 9: Multimodal Sensory Computing -- 9.1 Introduction -- 9.2 Multisensory Integration Modeling -- 9.2.1 Optimal Cue Integration -- 9.2.2 Normalization Model -- 9.2.3 Dynamic Adjustment -- 9.3 Hardware Implementation -- 9.4 Outlook -- References -- Index."]