SPECTRON: Streaming processor specific for adaptronic and biomeditronic applications

This paper presents a streaming processor specifically designed for adaptronic and biomedical engineering applications. The main characteristics of the streaming processor are the flexibility to implement floating-point-based scientific computations commonly performed in the digital signal processing application. The floating-point operators are connected to dual-port memories through separated 3 operand-buses and 2 resultant-buses. Synthesized with 130-nm technology, the Spectron can be clocked at 480 MHz. The processor can perform 4 parallel streaming/pipeline floating-point operations using its FPMAC and CORDIC cores, resulting in a performance of about 4×485 = 1.94 GFlops (Giga Floating-point operation per second), which is suitable for high performance image processing in biomedical electronic engineering applications.

[1]  A. Alvandpour,et al.  A 6.2-GFlops Floating-Point Multiply-Accumulator With Conditional Normalization , 2006, IEEE Journal of Solid-State Circuits.

[2]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[3]  Bertrand Delgutte,et al.  Hands-on learning in biomedical signal processing. , 2003, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[4]  K. Sridharan,et al.  50 Years of CORDIC: Algorithms, Architectures, and Applications , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[5]  F. Serra-Graells,et al.  A true-1-V 300-/spl mu/W CMOS-subthreshold log-domain hearing-aid-on-chip , 2004, IEEE Journal of Solid-State Circuits.

[6]  Wayne Luk,et al.  FPGA-based Streaming Computation for Lattice Boltzmann Method , 2007, 2007 International Conference on Field-Programmable Technology.

[7]  Jack E. Volder.  The CORDIC Trigonometric Computing Technique , 1959, IRE Trans. Electron. Comput..

[8]  C.C. Bissell,et al.  Signal processing for active control [Book Review] , 2002, IEEE Control Systems.

[9]  Joseph Zambreno,et al.  A hardware pipeline for accelerating ray traversal algorithms on streaming processors , 2010, 2010 IEEE 8th Symposium on Application Specific Processors (SASP).

[10]  M. Bellanger.  Adaptive filter theory: by Simon Haykin, McMaster University, Hamilton, Ontario L8S 4LB, Canada, in: Prentice-Hall Information and System Sciences Series, published by Prentice-Hall, Englewood Cliffs, NJ 07632, U.S.A., 1986, xvii+590 pp., ISBN 0-13-004052-5 025 , 1987 .

[11]  Dan H. Daggett,et al.  Decimal-Binary Conversions in CORDIC , 1959, IRE Trans. Electron. Comput..

[12]  Hartmut Janocha,et al.  Adaptronics and Smart Structures: Basics, Materials, Design, and Applications , 2007 .

[13]  B. Flachs,et al.  The microarchitecture of the synergistic processor for a cell processor , 2006, IEEE Journal of Solid-State Circuits.

[14]  Holger Hanselka,et al.  Adaptronische Systeme für automotive Anwendungen am Beispiel eines modularen, aktiven Strukturinterfaces (Smart Structures for Automotive Applications with the Example of a Modular Active Interface) , 2006, Autom..

[15]  A.M. Engebretson.  Benefits of digital hearing aids , 1994, IEEE Engineering in Medicine and Biology Magazine.