Name
Title | ||
|---|---|---|
MNSIM 2.0: A Behavior-Level Modeling Tool for Memristor-based Neuromorphic Computing Systems |
Abstract | ||
|---|---|---|
Memristor based neuromorphic computing systems give alternative solutions to boost the computing energy efficiency of Neural Network (NN) algorithms. Because of the large-scale applications and the large architecture design space, many factors will affect the computing accuracy and system's performance. In this work, we propose a behavior-level modeling tool for memristor-based neuromorphic computing systems, MNSIM 2.0, to model the performance and help researchers to realize an early-stage design space exploration. Compared with the former version and other benchmarks, MNSIM 2.0 has the following new features: 1. In the algorithm level, MNSIM 2.0 supports the inference accuracy simulation for mixed-precision NNs considering non-ideal factors. 2. In the architecture level, a hierarchical modeling structure for PIM systems is proposed. Users can customize their designs from the aspects of devices, interfaces, processing units, buffer designs, and interconnections. 3. Two hardware-aware algorithm optimization methods are integrated in MNSIM 2.0 to realize software-hardware co-optimization.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3386263.3407647 | GLSVLSI '20: Great Lakes Symposium on VLSI 2020
Virtual Event
China
September, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7944-1 | 3 |
PageRank | References | Authors |
0.44 | 0 | 13 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenhua Zhu | 1 | 51 | 7.74 |
| Hanbo Sun | 2 | 16 | 3.51 |
| Kaizhong Qiu | 3 | 3 | 0.77 |
| Lixue Xia | 4 | 182 | 15.55 |
| Gokul Krishnan | 5 | 24 | 7.77 |
| Guohao Dai | 6 | 89 | 8.17 |
| Dimin Niu | 7 | 609 | 31.36 |
| Xiaoming Chen | 8 | 43 | 13.67 |
| Xiaoming Chen | 9 | 43 | 13.67 |
| Hu Xiaobo Sharon | 10 | 6 | 0.82 |
| Yu Cao | 11 | 329 | 29.78 |
| Yuan Xie | 12 | 6430 | 407.00 |
| Yu Wang | 13 | 2279 | 211.60 |