|Title:||DL-RSIM: A simulation framework to enable reliable ReRAM-based accelerators for deep learning||Authors:||Lin, M.-Y.
|Issue Date:||2018||Source:||IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD||Abstract:||
Memristor-based deep learning accelerators provide a promising solution to improve the energy efficiency of neuromorphic computing systems. However, the electrical properties and crossbar structure of memristors make these accelerators error-prone. To enable reliable memristor-based accelerators, a simulation platform is needed to precisely analyze the impact of non-ideal circuit and device properties on the inference accuracy. In this paper, we propose a flexible simulation framework, DL-RSIM, to tackle this challenge. DL-RSIM simulates the error rates of every sum-of-products computation in the memristor-based accelerator and injects the errors in the targeted TensorFlow-based neural network model. A rich set of reliability impact factors are explored by DL-RSIM, and it can be incorporated with any deep learning neural network implemented by TensorFlow. Using three representative convolutional neural networks as case studies, we show that DL-RSIM can guide chip designers to choose a reliability-friendly design option and develop reliability optimization techniques. © 2018 ACM.
|URI:||https://scholars.lib.ntu.edu.tw/handle/123456789/487719||DOI:||10.1145/3240765.3240800||SDG/Keyword:||Acceleration; Computer aided design; Energy efficiency; Errors; Green computing; Memristors; Neural networks; Reliability; RRAM; Convolutional neural network; Cross-bar structures; Flexible simulation; Learning neural networks; Neural network model; Neuromorphic computing; Reliability optimization; Simulation framework; Deep learning
|Appears in Collections:||資訊工程學系|
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