Abstract
This chapter introduces the current state of the art of memristive devices and their roles in emerging computational paradigms. The materials systems and device characteristics of various resistive switching memories, also named memristive device technologies, including PCM, RRAM, MRAM, and FeRAM, are presented and discussed in terms of functionality, switching mechanism, and perspectives for applications beyond storage, toward brain-inspired computing. Conventional digital computers face increasing difficulties in performance and power efficiency due to their von Neumann architecture. Memristive devices are therefore considered as promising hardware building blocks for data and memory-centric computing schemes. This chapter provides an overview of currently enabled novel applications exploiting device properties.
| Original language | English (US) |
|---|---|
| Title of host publication | Memristive Devices for Brain-Inspired Computing |
| Subtitle of host publication | From Materials, Devices, and Circuits to Applications - Computational Memory, Deep Learning, and Spiking Neural Networks |
| Publisher | Elsevier |
| Pages | 3-16 |
| Number of pages | 14 |
| ISBN (Electronic) | 9780081027820 |
| DOIs | |
| State | Published - Jan 1 2020 |
| Externally published | Yes |
All Science Journal Classification (ASJC) codes
- General Engineering
Keywords
- Analog computing
- Deep neural networks
- FeFET
- FeRAM
- In-memory computing
- In-memory logic
- MRAM
- MTJ
- Memristive devices
- Neural networks
- Neuromorphic computing
- PCM
- RRAM
- Resistive switching memory
- Spiking neural networks