Abstract
Crossbar-based in-memory architectures have emerged as an attractive platform for energy-efficient realization of deep neural networks (DNNs). A key challenge in such architectures is achieving accurate and efficient writes due to the presence of bitcell conductance variations. In this paper, we propose the Single-Write In-memory Program-vErify (SWIPE) method that achieves high accuracy writes for crossbar-based in-memory architectures at 5×-to-10× lower cost than standard program-verify methods. SWIPE leverages the bit-sliced attribute of crossbar-based in-memory architectures and the statistics of conductance variations to compensate for device non-idealities. Using SWIPE to write into ReRAM crossbar allows for a 2× (CIFAR-10) and 3× (MNIST) increase in storage density with < 1% loss in DNN accuracy. In particular, SWIPE compensates for 4.8×-to-7.7× higher conductance variations. Furthermore, SWIPE can be augmented with injection-based training methods in order to achieve even greater enhancements in robustness.
Original language | English (US) |
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Article number | 9256661 |
Journal | IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD |
Volume | 2020-November |
DOIs | |
State | Published - Nov 2 2020 |
Event | 39th IEEE/ACM International Conference on Computer-Aided Design, ICCAD 2020 - Virtual, San Diego, United States Duration: Nov 2 2020 → Nov 5 2020 |
Keywords
- ReRAM
- compensation
- crossbars
- error correction
- in-memory
- neural networks
- resistive crossbars
- variations
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computer Graphics and Computer-Aided Design