DL-RSIM: A simulation framework to enable reliable ReRAM-based accelerators for deep learning
Journal
IEEE/ACM International Conference on Computer-Aided Design, Digest of Technical Papers, ICCAD
Date Issued
2018
Author(s)
Lin, M.-Y.
Cheng, H.-Y.
Lin, W.-T.
Yang, T.-H.
Tseng, I.-C.
Yang, C.-L.
Hu, H.-W.
Chang, H.-S.
Li, H.-P.
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.
SDGs
Other Subjects
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
Type
conference paper