Name
Title | ||
|---|---|---|
Concrete: A Per-Layer Configurable Framework For Evaluating Dnn With Approximate Operators |
Abstract | ||
|---|---|---|
Approximate computing has drawn considerable attention to both academia and industry in the area of DNN hardware. Despite substantial efforts to design approximate circuits and building blocks, the resilience of DNN layers and structures remains an untapped field to explore. This paper presents an efficient framework to evaluate DNN resilience with fine-grained approximate operations, such as multipliers, adders and low-bit operators. The framework can execute large-scale approximate DNNs with relatively less time overhead. Massive experiments are conducted with the proposed framework to reveal the relationship between network structures and error tolerance. Additionally, a case study of fine-tuning the approximate DNN is presented. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/icassp.2019.8682883 | 2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) |
Keywords | Field | DocType |
Approximate Computing, Neural Networks, Low-power Design, Optimization Framework | Kernel (linear algebra),Psychological resilience,Mathematical optimization,Adder,Error tolerance,Computer science,Operator (computer programming),Electronic circuit,Computer engineering,Approximate computing,Network structure | Conference |
ISSN | Citations | PageRank |
1520-6149 | 0 | 0.34 |
References | Authors | |
0 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheyu Liu | 1 | 10 | 6.55 |
| Guihong Li | 2 | 0 | 0.34 |
| Fei Qiao | 3 | 94 | 35.38 |
| Qi Wei | 4 | 49 | 20.68 |
| Ping Jin | 5 | 0 | 0.68 |
| Xin-Jun Liu | 6 | 35 | 10.04 |
| Huazhong Yang | 7 | 2239 | 214.90 |