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SoCDAL: System-on-chip design AcceLerator
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ACM Transactions on Design Automation of Electronic Systems (TODAES) archive
Volume 13 ,  Issue 1  (January 2008) table of contents
Article No. 17  
Year of Publication: 2008
ISSN:1084-4309
Authors
Yongjin Ahn  Seoul National University, Seoul
Keesung Han  Seoul National University, Seoul
Ganghee Lee  Seoul National University, Seoul
Hyunjik Song  Seoul National University, Seoul
Junhee Yoo  Seoul National University, Seoul
Kiyoung Choi  Seoul National University, Seoul
Xingguang Feng  Samsung Electronics, Kiheung
Publisher
ACM  New York, NY, USA
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ABSTRACT

Time-to-market pressure and the ever-growing design complexity of multiprocessor system-on-chips have demanded an efficient design environment that enables fast exploration of large design space. In this article, we introduce a new design environment, called SoCDAL, for accelerating multiprocessor system-on-chip design through fast design-space exploration targeting real-time multimedia systems. SoCDAL is a set of mostly automated tools covering system specification, hardware/software estimation, application-to-architecture mapping, simulation model generation, and system verification through simulation. For system specification, the process network model has been widely used for system specification because of its modeling capability. However, it is hard to use for real-time systems design, since its behavior cannot be estimated statically. We introduce a new approach which enables analyzing a process network model statically with some restrictions. For the hardware/software estimation, we analyze codes statically. Application-to-architecture mapping process implements a novel algorithm to support an arbitrary number of processors, with performance evaluation by static scheduling considering communication behavior. Mapping results are used to generate simulation models automatically at several transaction levels to be pipelined to a commercial tool. We show the effectiveness of our approaches by some experimental results with multimedia applications such as JPEG, H.263, and H.264 encoders, as well as an H.264 decoder.


REFERENCES

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1
 
2
Basten, T. and Hoogerbrugge, J. 2001. Efficient execution of process networks. In Communication Process Architectures, A. Chalmers et al., Eds. IOS Press, Bristol, UK, 1--14.
 
3
Benvenisite, A. and Berry, G. 1991. The synchronous approach to reactive and real-time systems. In Proc. IEEE (Sept.), 1270--1282.
 
4
Catapult C Synthesis. 2005. C-based design. http://www.mentor.com/products/c-based_design.
 
5
CDFG. 1998. Control data flow graph toolset. http://poppy.snu.ac.kr/CDFG.
 
6
 
7
ConvergenSC. 2004. ConvergenSC/Incisive design flow. http://www.coware.com.
 
8
Davis II, J., Hylands, C., Kienhuis, B., Lee, A., Liu, J., Liu, X., Muliadi, L., Neuendorffer, S., Tsay, J., Vogel, B., and Xiong, Y. 2001. Ptolemy II---Heterogeneous concurrent modeling and design in Java. Tech. Mem. M01/12J, UCB/ERL, Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA.
 
9
Dick, R. P. and Jha, N. K. 1998. MOGAC: A multiobjective genetic algorithm for hardware-software co-synthesis of distributed embedded systems. IEEE Trans. Comput.-Aided Des. Integr. Circ. Syst. 17, 920--935.
 
10
Dwivedi, B. K., Kumar, A., and Balakrishnan, M. 2003. Synthesis of application specific multiprocessor architectures for process networks. Tech. Rep., Department of Computer Science and Engineering, Indian Institute of Technology, Delhi, India.
 
11
 
12
 
13
Girault, A., Lee, B., and Lee, E. A. 1999. Hierarchical finite state machines with multiple concurrency models. IEEE Trans. Comput.-Aided Des. Integ. Circ. Syst. 18, 742--760.
 
14
GLPK. 1996. GNU linear programming kit. http://www.gnu.org/SW/glpk/glpk.html.
 
15
 
16
 
17
Hamann, A., Henia, R., Racu, R., Jersak, M., Richter, K., and Ernst, R. 2004. SymTA/S---Symbolic timing analysis for systems. In WIP Proceedings of the Euromicro Conference on Real-Time Systems, Catania, Italy, 17--20.
 
18
Han, K. and Kim, J. 2004. Quantum-Inspired evolutionary algorithms with a new termination criterion, Hϵ gate, and two phase scheme. IEEE Trans. Evol. Comput. 8, 156--169.
 
19
Han, K. and Kim, J. 2002. Quantum-Inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans. Evol. Comput. 6, 580--593.
 
20
 
21
 
22
Jantsch, A. and Sander, I. 2005. Models of computation and languages for embedded system design. IEE Proc. Comput. Digital Tech. 152, 114--129.
23
 
24
Kahn, G. 1974. The semantics of a simple language for parallel programming. In Proceedings of the IFIP Congress. The Netherlands, North-Holland, Amsterdam, 471--475.
 
25
 
26
Kirkpatrick, S., Gelatt, C. D., and Vecchi, M. P. 1983. Optimization by simulated annealing. Sci. 220, 671--680.
 
27
 
28
 
29
 
30
 
31
 
32
Liu, X., Liu, J., Eker, J., and Lee, E. A. 2002. Heterogeneous modeling and design of control systems. In Proceedings of the Conference on Software-Enabled Control: Information Technology for Dynamical Systems, T. Samad and G. Balas, Eds. Wiley-IEEE Press, New York, 105--122.
 
33
MaxSim. 1998. Arm RealView MaxSim. http://www.arm.com.
 
34
Micheli, G. D. and Gupta, R. K. 1997. Hardware/Software co-design. Proc. IEEE, 349--365.
 
35
MOTIF. 1995. Open Motif. http://www.opengroup.org/openmotif.
36
 
37
Pankert, M., Mauss, O., Ritz, S., and Meyr, H. 1994. Dynamic data flow and control flow in high level DSP code synthesis. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Aachen, Germany, 449--452.
 
38
Pasricha, S. 2002. Transaction level modeling of SoC using SystemC 2.0. In Synopsys User Group Conference, Bangalore, India. May.
 
39
 
40
 
41
 
42
Peace. 2006. PeaCE codesign environment. http://peace.snu.ac.kr/research/peace.
 
43
 
44
 
45
Prakash, S. and Parker, A. 1992. SOS: Synthesis of application-specific heterogeneous multiprocessor systems. J. Parallel Distrib. Comput. 16, 338--351.
 
46
Printz, H. 1991. Automatic mapping of large signal processing systems to a parallel machine. Tech. Memo. CMU-CS-91-101, School of Computer Science, Carnegie-Mellon University.
47
 
48
Shor, P. W. 1998. Quantum computing. Documenta Mathematica, Extra Volume ICM, 467--486.
 
49
SUIF1. 1996. The SUIF 1.x compiler system. http://suif.stanford.edu/suif/suif1/index.html.
 
50
SystemC. 2005. Open SystemC Initiative. http://www. systemc.org.
 
51
TEAK. 2003. CEVA DSP cores. http://www.ceva-dsp.com.
52
 
53
 
54

Collaborative Colleagues:
Yongjin Ahn: colleagues
Keesung Han: colleagues
Ganghee Lee: colleagues
Hyunjik Song: colleagues
Junhee Yoo: colleagues
Kiyoung Choi: colleagues
Xingguang Feng: colleagues