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Permuting streaming data using RAMs
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Journal of the ACM (JACM) archive
Volume 56 ,  Issue 2  (April 2009) table of contents
Article No. 10  
Year of Publication: 2009
ISSN:0004-5411
Authors
Markus Püschel  Carnegie Mellon University, Pittsburgh, Pennsylvania
Peter A. Milder  Carnegie Mellon University, Pittsburgh, Pennsylvania
James C. Hoe  Carnegie Mellon University, Pittsburgh, Pennsylvania
Publisher
ACM  New York, NY, USA
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ABSTRACT

This article presents a method for constructing hardware structures that perform a fixed permutation on streaming data. The method applies to permutations that can be represented as linear mappings on the bit-level representation of the data locations. This subclass includes many important permutations such as stride permutations (corner turn, perfect shuffle, etc.), the bit reversal, the Hadamard reordering, and the Gray code reordering.

The datapath for performing the streaming permutation consists of several independent banks of memory and two interconnection networks. These structures are built for a given streaming width (i.e., number of inputs and outputs per cycle) and operate at full throughput for this streaming width.

We provide an algorithm that completely specifies the datapath and control logic given the desired permutation and streaming width. Further, we provide lower bounds on the achievable cost of a solution and show that for an important subclass of permutations our solution is optimal.

We apply our algorithm to derive datapaths for several important permutations, including a detailed example that carefully illustrates each aspect of the design process. Lastly, we compare our permutation structures to those of Järvinen et al. [2004], which are specialized for stride permutations.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Astola, J., and Akopian, D. 1999. Architecure-oriented regular algorithms for discrete sine and cosine transforms. IEEE Trans. Sig. Proc. 47, 4, 1109--1124.
 
2
Beauchamp, K. G. 1984. Applications of Walsh and Related Functions. Academic Press, Orlando, FL.
 
3
Benes, V. E. 1965. Mathematical Theory of Connecting Networks and Telephone Traffic. Academic Press, Orlando, FL.
 
4
Bernstein, D. S. 2005. Matrix Mathematics. Princeton University Press, Princeton, NJ.
 
5
 
6
Bürgisser, P., Clausen, M., and Shokrollahi, M. A. 1997. Algebraic Complexity Theory. Springer-Verlag, Berlin, Germany.
 
7
Duhamel, P. 1990. A connection between bit reversal and matrix transposition: Hardware and software consequences. IEEE Trans. Acous., Speech, Signal Proc. 38, 11, 1893--1418.
 
8
Gorman, S. F., and Wills, J. M. 1995. Partial column FFT pipelines. IEEE Trans. Circ. Syst. II: Analog Digital Signal Proc. 42, 6, 414--423.
 
9
 
10
 
11
 
12
13
 
14
Milder, P. A., Hoe, J. C., and Püschel, M. 2009. Automatic generation of streaming datapaths for arbitrary fixed permutations. In Proceedings of Design, Automation and Test in Europe.
15
 
16
Parhi, K. K. 1992. Systematic synthesis of DSP data format converters using life-time analysis and forward-backward register allocation. IEEE Trans. Circ. Syst. II: Analog Digital Signal Proc. 39, 7, 423--440.
 
17
 
18
 
19
Püschel, M., and Moura, J. M. F. 2008. Algebraic signal processing theory: Cooley-Tukey type algorithms for DCTs and DSTs. IEEE Trans. Signal Proc. 56, 4, 1502--1521.
 
20
Takala, J. H., Järvinen, T. S., and Sorokin, H. T. 2003. Conflict-free parallel memory access scheme for FFT processors. In Proceedings of the 2003 International Symposium on Circuits and Systems.
 
21
 
22
Viterbi, A. J. 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory 13, 2, 260--269.
23

Collaborative Colleagues:
Markus Püschel: colleagues
Peter A. Milder: colleagues
James C. Hoe: colleagues