Intrinsic Evolution of Truncated Puiseux Series on a Mixed-Signal Field-Programmable SoC

Mixed-signal system-on-chip (SoC) devices offer single-chip solutions, but face challenges of hardware-software co-design optimization, device signal range constraints, and limited precision. These issues are addressed by developing a multi-level evolutionary approach to realize complex computational circuits called Embedded-Cascaded Hierarchically Evolved Logic Output Networks (ECHELON). The ECHELON technique utilizes analog evolved building blocks and refines their output using digital fabric to compose power series expansions of transcendental functions which are all routed under intrinsic control on a field-programmable SoC (PSoC). The result for the evolution of seven different powers of the independent variable is a reduction of 31.24% in the overall error as compared to the analog circuits that produce the raw inputs to a differential digital correction phase. Computation blocks developed on a Cypress PSoC-5LP mixed-signal SoC reduced error in the final mathematical approximation to the range of 40–150 mV. In doing so, speedups of roughly 1.4-fold to 6.6-fold with an average of 2.72-fold reduction in function execution times were attained. In particular, this approach achieved a 41.7-fold reduction in error with respect to the largest power of the independent variable used as an input to compute an erf(x) function.

[1]  Abhisek Ukil,et al.  Fast computation of arctangent functions for embedded applications: A comparative analysis , 2011, 2011 IEEE International Symposium on Industrial Electronics.

[2]  Vignesh Thangavel,et al.  Self-Scaling Evolution of analog computation circuits with digital accuracy refinement , 2015, 2015 NASA/ESA Conference on Adaptive Hardware and Systems (AHS).

[3]  Weng-Fai Wong,et al.  Fast Evaluation of the Elementary Functions in Single Precision , 1995, IEEE Trans. Computers.

[4]  Arjun Suresh,et al.  Intercepting Functions for Memoization , 2015, ACM Trans. Archit. Code Optim..

[5]  C. Hoffmann Algebraic curves , 1988 .

[6]  David V. Anderson,et al.  Cooperative analog-digital signal processing , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[7]  Israel Koren,et al.  Evaluating Elementary Functions in a Numerical Coprocessor Based on Rational Approximations , 1990, IEEE Trans. Computers.

[8]  Y. Qi,et al.  Insufficiency of piecewise evolution , 2001, Proceedings Third NASA/DoD Workshop on Evolvable Hardware. EH-2001.

[9]  Adrien Poteaux,et al.  Improving Complexity Bounds for the Computation of Puiseux Series over Finite Fields , 2015, ISSAC.

[10]  Vignesh Thangavel Cascaded Digital Refinement for Intrinsic Evolvable Hardware , 2015 .

[11]  Melanie Mitchell,et al.  The royal road for genetic algorithms: Fitness landscapes and GA performance , 1991 .

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

[13]  Ronald F. DeMara,et al.  Layered Approach to Intrinsic Evolvable Hardware using Direct Bitstream Manipulation of Virtex II Pro Devices , 2007, 2007 International Conference on Field Programmable Logic and Applications.

[14]  Gunar Schirner,et al.  Function-Level Processor (FLP): A High Performance, Minimal Bandwidth, Low Power Architecture for Market-Oriented MPSoCs , 2014, IEEE Embedded Systems Letters.

[15]  Hitoshi Iba,et al.  Evolving analog circuits by variable length chromosomes , 2003 .

[16]  Jooheung Lee,et al.  Self-Adapting Resource Escalation for Resilient Signal Processing Architectures , 2014, J. Signal Process. Syst..

[17]  Raimund Kirner,et al.  Comparing WCET and Resource Demands of Trigonometric Functions Implemented as Iterative Calculations vs. Table-Lookup , 2006, WCET.

[18]  Andrew M. Tyrrell,et al.  Challenges of evolvable hardware: past, present and the path to a promising future , 2011, Genetic Programming and Evolvable Machines.