Name
Title | ||
|---|---|---|
DNN+NeuroSim V2.0: An End-to-End Benchmarking Framework for Compute-in-Memory Accelerators for On-Chip Training |
Abstract | ||
|---|---|---|
DNN+NeuroSim is an integrated framework to benchmark compute-in-memory (CIM) accelerators for deep neural networks, with hierarchical design options from device-level, to circuit level and up to algorithm level. A python wrapper is developed to interface NeuroSim with a popular machine learning platform: Pytorch, to support flexible network structures. The framework provides automatic algorithm-to... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TCAD.2020.3043731 | IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems |
Keywords | DocType | Volume |
Training,Common Information Model (computing),System-on-chip,Computer architecture,Hardware,Benchmark testing,Integrated circuit modeling | Journal | 40 |
Issue | ISSN | Citations |
11 | 0278-0070 | 1 |
PageRank | References | Authors |
0.37 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaochen Peng | 1 | 61 | 12.17 |
| Huang Shanshi | 2 | 1 | 0.37 |
| Jiang Hongwu | 3 | 1 | 0.37 |
| Lu Anni | 4 | 3 | 1.79 |
| Shimeng Yu | 5 | 490 | 56.22 |