Architectural approach to alternate low-level primitive structures (ALPS) for acoustic signal processing

Architectural approach to alternate low-level primitive structures (ALPS) for acoustic signal processing

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The input/output relationship describing a given signal-processing application in its entirety can be partitioned into a set of primitive operations which may be represented using signal-processing graph notation (SPGN) as a network of interconnected processes. A set of alternate low-level primitive structures (ALPS) can be defined to implement the operations underpinning each node in this network. This SPGN/ALPS processor network is queue driven and message based, each queue consisting of a message header and an array of data. Current systems developments have demonstrated that a small, finite set of low-level primitives is sufficient for the synthesis of the majority of acoustic-processing problems, and the paper outlines the design approach for some of these. In particular, a prime-radix DFT algorithm, FIR and IIR filter structures using constrained filter coefficients and a general purpose array address mapping element are described. In addition, system architectures based on these SPGN/ALPS networks are developed.


    1. 1)
      • Wu, Y.S.: `Architectural considerations of a signal processor under microprogram control', Proc. AFIPS Spring Joint Computer Conference, 1972, Atlantic City, New Jersey, 40, p. 675–683.
    2. 2)
      • Kratz, G.L., Sproul, W.W., Walendziewicz, E.T.: `A microprogrammed approach to signal processing', IEEE C-23, 1974, p. 808–816.
    3. 3)
      • Wu, Y.S.: `A common operational software (ACOS) approach to a signal processing development system', Proc. IEEE International Conference on Acoustics, 1983, Boston, p. 1172–1175, speech and signal processing.
    4. 4)
      • R.M. Karp , R.E. Miller . Properties of a model for parallel computations: determinacy, termination, queueing. SIAM J. Appl. Math. , 1390 - 1411
    5. 5)
      • Curtis, T.E.: `A modular approach to signal processing', Proc. Real-time general-purpose, 1979, La Spezia, Italy, SACLANT ASW Research Centre, high-speed signal processing systems for underwater research.
    6. 6)
      • Defence attache, 1983, (5), pp. 25–27.
    7. 7)
      • Knudsen, M.J.: `MUSEC, a powerful network of signal microprocessors', Proc. IEEE International Conference on Acoustics, 1983, Boston, p. 431–434, speech and signal processing.
    8. 8)
      • T.E. Curtis , J.T. Wickenden . Hardware-based Fourier transforms: algorithms and architectures. IEE Proc. F, Commun., Radar & Signal Process , 423 - 432
    9. 9)
      • Lim, Y.C., Parker, S.R., Constantinides, A.G.: `Finite word length FIR filter design using integer programming over a discrete coefficient space', IEEE ASSP-30, 1982, p. 661–664.
    10. 10)
      • Lim, Y.C., Constantinides, A.G.: `Linear phase FIR digital filter without multiplies', Proc. IEE International Symposium on Circuits and systems, 1979, Tokyo, p. 185–188.
    11. 11)
      • Shively, R.R.: `A digital processor to generate spectra in real time', IEEE C-17, 1968, p. 485–491.

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