Neuro-inspired Computing Using Resistive Synaptic Devices

• 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: Engineering.; Microprocessors.; Electronic circuits.; Circuits and Systems.; Electronic Circuits and Devices.; Processor Architectures.

 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 summaries of resistive synaptic devices, from the individual cell characteristics to the large-scale array integration. This book also discusses peripheral neuron circuits design challenges and design strategies. Finally, the authors describe the impact of device non-ideal properties (e.g. noise, variation, yield) and their impact on the learning performance at the system-level, using a device-algorithm co-design methodology. • Provides single-source reference to recent breakthroughs in resistive synaptic devices, not only at individual cell-level, but also at integrated array-level; • Includes detailed discussion of the peripheral circuits and array architecture design of the neuro-crossbar system; • Focuses on new experimental results that are likely to solve practical, artificial intelligent problems, such as image classification. 

 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.