Featured in DATE 2023

eF²lowSim: System-Level Simulator of eFlash-Based Compute-in-Memory Accelerators for Convolutional Neural Networks

A new system-level simulator, eF²lowSim, is proposed to estimate the bit-accurate and cycle-accurate performance of eFlash compute-in-memory (CIM) accelerators for convolutional neural networks. The eF²lowSim can predict the inference accuracy by considering the impact of circuit nonideality such as program disturbance. Moreover, the eF²lowSim can also evaluate the system-level performance of dataflow strategies that have a significant impact on hardware area and performance of eFlash CIM accelerators. The simulator helps to find the optimal dataflow strategy of an eFlash CIM accelerator for each convolutional layer. It is shown that the improvement of area efficiency amounts to 26.8%, 21.2% and 17.9% in the case of LeNet-5, VGG-9 and ResNet-18, respectively.

Source:

https://doi.org/10.23919/DATE56975.2023.10137200