Name
Abstract | ||
|---|---|---|
Programming Micron's Automata Processor (AP) requires expertise in both automata theory and the AP architecture, as programmers have to manually manipulate state transition elements (STEs) and their transitions with a low-level Automata Network Markup Language (ANML). When the required STEs of an application exceed the hardware capacity, multiple reconfigurations are needed. However, most previous AP-based designs limit the dataset size to fit into a single AP board and simply neglect the costly overhead of reconfiguration. This results in unfair performance comparisons between the AP and other processors. To address this issue, we propose a framework for the fast and fair evaluation of AP devices. Our framework provides a hierarchical approach that automatically generates automata for large datasets through user-defined paradigms and allows the use of cascadable macros to achieve highly optimized reconfigurations. We highlight the importance of counting the configuration time in the overall AP performance, which in turn, can provide better insight into identifying essential hardware features, specifically for large-scale problem sizes. Our framework shows that the AP can achieve up to 461x overall speedup fairly compared to CPU counterparts. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IISWC.2017.8167767 | 2017 IEEE International Symposium on Workload Characterization (IISWC) |
Keywords | Field | DocType |
Micron's Automata Processor,automata processing hardware,essential hardware features,AP performance,highly optimized reconfigurations,AP devices,processors,unfair performance comparisons,single AP board,dataset size,multiple reconfigurations,hardware capacity,required STEs,low-level Automata Network Markup Language,state transition elements,AP architecture,automata theory | Automata theory,Computer science,Automaton,Macro,Computer hardware,Pattern matching,Control reconfiguration,Speedup,Markup language | Conference |
ISBN | Citations | PageRank |
978-1-5386-1234-7 | 1 | 0.35 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaodong Yu | 1 | 34 | 2.17 |
| Kaixi Hou | 2 | 85 | 5.85 |
| Hao Wang | 3 | 402 | 24.64 |
| Wu-chun Feng | 4 | 2812 | 232.50 |