IR-QNN Framework: An IR Drop-Aware Offline Training Of Quantized Crossbar Arrays
Permanent link to this recordhttp://hdl.handle.net/10754/666368
MetadataShow full item record
AbstractResistive Crossbar Arrays present an elegant implementation solution for Deep Neural Networks acceleration. The Matrix-Vector Multiplication, which is the corner-stone of DNNs, is carried out in O(1) compared to O(N2) steps for digital realizations of O(log2(N)) steps for in-memory associative processors. However, the IR drop problem, caused by the inevitable interconnect wire resistance in RCAs remains a daunting challenge. In this paper, we propose a fast and efficient training and validation framework to incorporate the wire resistance in Quantized DNNs, without the need for computationally extensive SPICE simulations during the training process. A fabricated four-bit Au/Al2O3/HfO2/TiN device is modelled and used within the framework with two-mapping schemes to realize the quantized weights. Efficient system-level IR-drop estimation methods are used to accelerate training. SPICE validation results show the effectiveness of the proposed method to capture the IR drop problem achieving the baseline accuracy with a 2% and 4% drop in the worst-case scenario for MNIST dataset on multilayer perceptron network and CIFAR 10 dataset on modified VGG and AlexNet networks, respectively. Other nonidealities, such as stuck-at fault defects, variability, and aging, are studied. Finally, the design considerations of the neuronal and the driver circuits are discussed.
CitationFouda, M. E., Lee, S., Lee, J., Kim, G. H., Kurdahi, F., & Eltawil, A. (2020). IR-QNN Framework: An IR Drop-Aware Offline Training Of Quantized Crossbar Arrays. IEEE Access, 1–1. doi:10.1109/access.2020.3044652
Except where otherwise noted, this item's license is described as This work is licensed under a Creative Commons Attribution 4.0 License.