Adaptive and Evolvable Hardware - A Multifaceted Analysis

The paper explores adaptive and evolvable hardware (AEH) solutions from three essential perspectives: (a) technology, (b) economics and (c) system architecture. After setting the basis for the AEH terminology and taxonomy, the paper takes a look at the "market" and what adaptation is expected to provide to satisfy user needs and to solve real-world problems. Technologies that offer adaptation are explored, seeking common principles and techniques beneath the wide diversity of application areas. The focus here is on hardware that derives its flexibility from reconfiguration. The paper continues with an economic perspective and how adaptability offers better solutions not only for end users, but also for product providers. Finally, it explores system architecture ideas, to which biology offers continuous inspiration and may drive future architectural developments. Directions with maximal impact in advancing AEH appear to focus towards (1) reducing cost per function, (2) reducing reconfiguration overhead, (3) increasing re-programming speed, (4) improving algorithmic efficiency, (5) embedding self-reconfiguration algorithms into reconfigurable matter (or, more general, diffusing intelligence to finest HW levels), (6) ensuring that the system goes through safe states during adaptation and evolution, (7) and creating a specific development language and dedicated development tools.

[1]  Walid A. Najjar,et al.  A Compiler Intermediate Representation for Reconfigurable Fabrics , 2006, FPL.

[2]  Jim Torresen,et al.  Possibilities and Limitations of Applying Evolvable Hardware to Real-World Applications , 2000, FPL.

[3]  Erfu Yang,et al.  ESPACENET: A Framework of Evolvable and Reconfigurable Sensor Networks for Aerospace–Based Monitoring and Diagnostics , 2006, First NASA/ESA Conference on Adaptive Hardware and Systems (AHS'06).

[4]  Adrian Stoica.  ON HARDWARE EVOLVABILITY AND LEVELS OF GRANULARITY , 1997 .

[5]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[6]  J. Tørresen,et al.  Increased complexity evolution applied to evolvable hardware , 1999 .

[7]  Viktor Mikhaĭlovich Glushkov,et al.  An Introduction to Cybernetics , 1957, The Mathematical Gazette.

[8]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[9]  Adrian Stoica,et al.  Polymorphic Electronics , 2001, ICES.

[10]  Witold Pedrycz,et al.  Fuzzy neural networks and neurocomputations , 1993 .

[11]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[12]  Adrian Stoica,et al.  Self-Reconfigurable Analog Arrays: Off-The Shelf Adaptive Electronics for Space Applications , 2007, Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007).

[13]  Tughrul Arslan,et al.  An AMBA AHB-based reconfigurable SoC architecture using multiplicity of dedicated flyby DMA blocks , 2005, Proceedings of the ASP-DAC 2005. Asia and South Pacific Design Automation Conference, 2005..

[14]  Brian D. O. Anderson,et al.  Failures of adaptive control theory and their resolution , 2005, Commun. Inf. Syst..

[15]  Vasile Palade,et al.  Computational Intelligence - Engineering of Hybrid Systems , 2010, Studies in Fuzziness and Soft Computing.

[16]  Marco Tomassini,et al.  A phylogenetic, ontogenetic, and epigenetic view of bio-inspired hardware systems , 1997, IEEE Trans. Evol. Comput..

[17]  Hitoshi Iba,et al.  Applying Evolvable Hardware to Autonomous Agents , 1994, PPSN.

[18]  Marco Tomassini,et al.  Towards Evolvable Hardware , 1996, Lecture Notes in Computer Science.

[19]  Adrian Stoica,et al.  Embedded Reconfigurable Array Fabrics for Efficient Implementation of Image Compression Techniques , 2006, First NASA/ESA Conference on Adaptive Hardware and Systems (AHS'06).