Digital associative memory for word-parrallel Manhattan-distance-based vector quantization

@article{Sasaki2012DigitalAM,
  title={Digital associative memory for word-parrallel Manhattan-distance-based vector quantization},
  author={Seiryu Sasaki and Masahiro Yasuda and Hans J{\"u}rgen Mattausch},
  journal={2012 Proceedings of the ESSCIRC (ESSCIRC)},
  year={2012},
  pages={185-188}
}
Digital Word-parallel associative-memory architecture capable of Manhattan-distance-based vector quantization is reported, which applies frequency dividers and clock counting to realize nearest Manhattan-distance (MD) search. Experimental verification was done with a 65 nm CMOS design implementing 128 reference vectors, each having 16 components and 16 bit per component. For the fabricated test chips 926 ps minimum search time and 2.13 mW power dissipation are measured at 120MHz and Vdd = 1.2V… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-6 of 6 references

Cicc2004

  • Y. Oike, M. Ikeda, K. Asada
  • pp. 295-298
  • 2004
Highly Influential
9 Excerpts

Ssdm'2002

  • Y. Yano, T. Koide, H. J. Mattausch
  • pp. 254-255
  • 2002
Highly Influential
6 Excerpts

Symp

  • H. J. Mattausch, M. Yasuda, A. Kawabata, W. Imafuku, T. Koide, VLSI Circ
  • 2011, pp. 192-195
  • 2011
1 Excerpt

Ssdm'2006

  • M. A. Abedin, Y. Tanaka, A. Ahmadi, T. Koide, H. J. Mattausch
  • pp. 282-283
  • 2006
1 Excerpt

Tveter “The Pattern Recognition Basis of Artificial Intelligence,

  • D R.
  • 1997
1 Excerpt

pp

  • E. A. Vittoz, B. Gerber, F. Leuenberger, IEEE J. Solid-State Circuits
  • 100-104
  • 1972

Similar Papers

Loading similar papers…