Neuro-inspired Computing Using Resistive Synaptic Devices

This book summarizes the recent breakthroughs in hardware implementation of neuro-inspired computing using resistive synaptic devices. The authors describe how two-terminal solid-state resistive memories can emulate synaptic weights in a neural network. Readers will benefit from state-of-the-art sum...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Yu, Shimeng (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2017.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Chapter1: Introduction to Neuro-Inspired Computing using Resistive Synaptic Devices
  • Part I: Device-level Demonstrations of Resistive Synaptic Devices
  • Chapter2: Phase Change Memory based Synaptic Devices
  • Chapter3: Pr0.7Ca0.3MnO3 (PCMO) based Synaptic Devices
  • Chapter4: TaOx/TiO2 based Synaptic Devices
  • Part II: Array-level Demonstrations of Resistive Synaptic Devices and Neural Networks
  • Chapter5: Training and Inference in Hopfield Network using 10×10 Phase Change Synaptic Array
  • Chapter6: Experimental Demonstration of Firing-Rate Neural Networks based on Metal-Oxide Memristive Crossbars
  • Chapter7: Weight Tuning of Resistive Synaptic Devices and Convolution Kernel Operation on 12×12 Cross-Point Array
  • Chapter8: Spiking Neural Network with 256×256 PCM Array
  • Part III: Circuit, Architecture and Algorithm-level Design of Resistive Synaptic Devices based Neuromorphic System
  • Chapter9: Peripheral Circuit Design Considerations of Neuro-inspired Architectures
  • Chapter10: Processing-in-Memory Architecture Design for Accelerating Neuro-Inspired Algorithms
  • Chapter11: Multi-layer Perceptron Algorithm: Impact of Non-Ideal Conductance and Area-Efficient Peripheral Circuits
  • Chapter12: Impact of Non-Ideal Resistive Synaptic Device Behaviors on Implementation of Sparse Coding Algorithm
  • Chapter13: Binary OxRAM/CBRAM Memories for Efficient Implementations of Embedded Neuromorphic Circuits.