### Abstract

A VLSI architecture for separable kernel multi-dimensional transforms is described. What is novel about the architecture is its data rotator, which is a hexagonal mesh of processors. The rotator is completely scalable and modular and is programmable with respect to d and the length of each dimension. The proposed architecture has an AT^{2} figure of O(d^{2}n^{2} log^{2} n), where d is the dimensionality, n is the total number of elements in the data cube, and the precision of an element is assumed to be Θ(log n). The value of AT^{2} for the rotator itself is O(n^{2} log^{2} n) for a single rotation, which is optimal. Multi-dimensional separable kernel transforms may be computed by performing d sets of 1-D transforms, each along a unique axis of the d-D data cube. A natural architecture for such problems consists of a number of 1-D transform processors and a rotator or transposer.

Original language | English (US) |
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Title of host publication | Plenary, Special, Audio, Underwater Acoustics, VLSI, Neural Networks |

Publisher | Publ by IEEE |

Volume | 1 |

ISBN (Print) | 0780309464 |

Publication status | Published - 1993 |

Event | 1993 IEEE International Conference on Acoustics, Speech and Signal Processing - Minneapolis, MN, USA Duration: Apr 27 1993 → Apr 30 1993 |

### Other

Other | 1993 IEEE International Conference on Acoustics, Speech and Signal Processing |
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City | Minneapolis, MN, USA |

Period | 4/27/93 → 4/30/93 |

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### All Science Journal Classification (ASJC) codes

- Signal Processing
- Electrical and Electronic Engineering
- Acoustics and Ultrasonics

### Cite this

*Plenary, Special, Audio, Underwater Acoustics, VLSI, Neural Networks*(Vol. 1). Publ by IEEE.