دورية أكاديمية
Optimization of Projected Phase Change Memory for Analog In‐Memory Computing Inference
| العنوان: | Optimization of Projected Phase Change Memory for Analog In‐Memory Computing Inference |
|---|---|
| المؤلفون: | Ning Li, Charles Mackin, An Chen, Kevin Brew, Timothy Philip, Andrew Simon, Iqbal Saraf, Jin‐Ping Han, Syed Ghazi Sarwat, Geoffrey W. Burr, Malte Rasch, Abu Sebastian, Vijay Narayanan, Nicole Saulnier |
| المصدر: | Advanced Electronic Materials, Vol 9, Iss 6, Pp n/a-n/a (2023) |
| بيانات النشر: | Wiley-VCH, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Electric apparatus and materials. Electric circuits. Electric networks LCC:Physics |
| مصطلحات موضوعية: | deep neural network, in‐memory computing, non‐volatile memory, phase change memory, resistance drift, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4, Physics, QC1-999 |
| الوصف: | Abstract Phase change memory (PCM) is one of the most promising candidates for non‐von Neumann based analog in‐memory computing–particularly for inference of previously‐trained deep neural networks (DNN). It is shown that PCM electrical properties can be tuned systematically using a projection liner, which is designed for resistance drift mitigation, in the manufacturable mushroom PCM. A systematic study of the electrical properties‐including resistance values, memory window, resistance drift, read noise, and their impact on the accuracy of large neural networks of various types and with tens of millions of weights is performed. It is sown that the DNN accuracy can be improved by the PCM with liner for both the short term and long term after programming, due to reduced resistance drift and read noise, respectively, despite the trade‐off of reduced memory window. The liner conductance, PCM device characteristics, and network inference accuracy with PCM memory window and reset state conductance is correlated, which allows us to identify the device optimization space to achieve better short term and long term accuracy for large neural networks. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2199-160X |
| Relation: | https://doaj.org/toc/2199-160X |
| DOI: | 10.1002/aelm.202201190 |
| URL الوصول: | https://doaj.org/article/21e46ddebffb4e81b334c7208c700cea |
| رقم الأكسشن: | edsdoj.21e46ddebffb4e81b334c7208c700cea |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 2199160X |
|---|---|
| DOI: | 10.1002/aelm.202201190 |