Name
Abstract | ||
|---|---|---|
Applications typically exhibit extremely different performance characteristics depending on the accelerator. Back propagation neural network (BPNN) has been parallelized into different platforms. However, it has not yet been explored on speculative multicore architecture thoroughly. This paper presents a study of parallelizing BPNN on a speculative multicore architecture, including its speculative execution model, hardware design and programming model. The implementation was analyzed with seven well-known benchmark data sets. Furthermore, it trades off several important design factors in coming speculative multicore architecture. The experimental results show that: (1) the BPNN performs well on speculative multicore platform. It can achieve similar speedup (17.7x to 57.4x) compared with graphics processors (GPU) while provides a more friendly programmability. (2) 64 cores' computing resources can be used efficiently and 4k is the proper speculative buffer capacity in the model. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/ICPADS.2016.119 | 2016 IEEE 22ND INTERNATIONAL CONFERENCE ON PARALLEL AND DISTRIBUTED SYSTEMS (ICPADS) |
Keywords | Field | DocType |
thread level speculation, parallel programming, back propagation, multicore | Computer science,Back propagation neural network,Speculative multithreading,Real-time computing,Multi-core processor,Distributed computing,Speedup,Graphics,Computer architecture,Programming paradigm,Speculative execution,Parallel computing,Backpropagation | Conference |
ISSN | Citations | PageRank |
1521-9097 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaobin Wang | 1 | 0 | 0.34 |
| Hong An | 2 | 1 | 1.73 |
| Zhi-qin Liu | 3 | 12 | 4.93 |
| Tao Liu | 4 | 0 | 0.34 |
| Dongmei Zhao | 5 | 0 | 0.34 |