The Wavelet Chip Project

The first set of papers relates to the Wavechip project, an attempt to implement the continuous wavelet transform (CWT) in silicon, using analog VLSI technology. The CWT is implemented as a filter bank having a gaussian kernel on each channel, and center frequencies spaced logarithmically in frequency space. The output of each channel is sampled in proportion to the bandwidth of the channel, making this a multiresolution scheme that breaks incoming signals into a compact description in both time and frequency. For time-varying signals, this description is more accurate than an FFT decomposition, and like the discrete wavelet transform (DWT) it should be applicable to data compression.

Three papers (having a great deal of overlap) detail the creation of the chip through several chip implementations and also the ICNN '93 conference in San Francisco (Int'l Conference on Neural Networks). These papers are listed below:

  1. WaveChip version 1 describes the first chip implementation to be fabricated.
  2. WaveChip version 2 describes the second chip implementation to be fabricated, including on-board sampling.
  3. WaveChip version 3 describes the third chip implementation to be fabricated, with a number of improvements over the previous designs but without the on-board sampling.
The wavechip project continued with a fourth iteration based on mixed analog and digital technology. One paper was presented at the NIPS '95 conference (in Colorado, in November) (available as, and another was published in Express Letters of IEEE Circuits and Systems, August, 1996: ``Oversampling Architecture for Analog Harmonic Modulation'' by R. Timothy Edwards and Gert Cauwenberghs. This chip has been designed and fabricated, and the ingenious modulation scheme works beautifully.

* Discrete Wavelet Transform Processor

This project, completed during the summer of 1991, is an implementation of the 2 dimensional discrete wavelet transform (DWT) on a commercially available DSP. The transform is computed in parallel and scheduling is performed which yields the minimum latency between the input and the transformed output.

Results of this project encouraged Dr. Michael Godfrey and me to start designing a continuous wavelet transform implementation.

The paper is found here: Wavelet paper.

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Last updated: April 9, 2012 at 2:21pm