Name
Abstract | ||
|---|---|---|
The von Neumann bottleneck and memory wall have posed fundamental limitations in latency and energy consumption of modern computers based on von Neumann architecture. In-memory computing represents a radical shift in the computer architecture that can address such problems by merging computing functions within the memory itself. In this article, we review the emerging nonvolatile memory devices, such as resistance-based and charge-based memory devices, that are explored for in-memory computing applications. We will provide an overview of the materials, mechanisms, and integration of these devices, and discuss the optimizations at the device and array levels that are required to better support in-memory computing. Recent progress in the application of in-memory computing in artificial neural networks, spiking neural networks, digital logic in memory as well as hardware security will also be discussed. Finally, we will discuss the remaining challenges in this field and potential pathways to address them. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s11432-021-3327-7 | SCIENCE CHINA-INFORMATION SCIENCES |
Keywords | DocType | Volume |
in-memory computing, von Neumann bottleneck, nonvolatile memory, energy efficiency, neural network | Journal | 64 |
Issue | ISSN | Citations |
12 | 1674-733X | 1 |
PageRank | References | Authors |
0.35 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Caidie Cheng | 1 | 1 | 1.37 |
| Pek Jun Tiw | 2 | 1 | 0.35 |
| Yimao Cai | 3 | 9 | 7.26 |
| Xiaoqin Yan | 4 | 1 | 0.35 |
| Yuchao Yang | 5 | 4 | 4.80 |
| Ru Huang | 6 | 188 | 48.74 |