Automatic Design Space Exploration of Approximate Algorithms for Big Data Applications

2016 ◽  
Author(s):  
Mario Barbareschi ◽  
Federico Iannucci ◽  
Antonino Mazzeo
Keyword(s):  
Big Data ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Automatic Design ◽  
Approximate Algorithms ◽  
Big Data Applications

Automatic Design Space Exploration of Register Bypasses in Embedded Processors

2007 ◽  
Vol 26 (12) ◽  
pp. 2102-2115 ◽  
Author(s):  
A. Shrivastava ◽  
Park Sanghyun ◽  
E. Earlie ◽  
N.D. Dutt ◽  
A. Nicolau ◽  
...  
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Embedded Processors ◽  
Automatic Design

scholarly journals ArchExplorer for Automatic Design Space Exploration

IEEE Micro ◽  
2010 ◽  
Vol 30 (5) ◽  
pp. 5-15 ◽  
Author(s):  
Veerle Desmet ◽  
Sylvain Girbal ◽  
Alex Ramirez ◽  
Olivier Temam ◽  
Augusto Vega
Keyword(s):  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Automatic Design

scholarly journals Towards Multi-objective Optimization of Automatic Design Space Exploration for Computer Architecture through Hyper-heuristic

2019 ◽  
Vol 9 (3) ◽  
pp. 4292-4297
Author(s):  
M. Latif ◽  
M. A. Ismail
Keyword(s):  
Computer Architecture ◽  
Design Space Exploration ◽  
Design Space ◽  
Space Exploration ◽  
Automatic Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Heuristic Technique ◽  
Np Hard Problem

Multi-objective optimization is an NP-hard problem. ADSE (automatic design space exploration) using heuristics has been proved to be an appropriate method in resolving this problem. This paper presents a hyper-heuristic technique to solve the DSE issue in computer architecture. Two algorithms are proposed. A hyper-heuristic layer has been added to the FADSE (framework for automatic design space exploration) and relevant algorithms have been implemented. The benefits of already existing multi-objective algorithms have been joined in order to strengthen the proposed algorithms. The proposed algorithms, namely RRSNS (round-robin scheduling NSGA-II and SPEA2) and RSNS (random scheduling NSGA-II and SPEA2) have been evaluated for the ADSE problem. The results have been compared with NSGA-II and SPEA2 algorithms. Results show that the proposed methodologies give competitive outcomes in comparison with NSGA-II and SPEA2.

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