  Researchers traditionally solve the computational problems through rigorous
and deterministic algorithms called as Hard Computing. These precise algorithms
have widely been realized using digital technology as an inherently reliable
and accurate implementation platform, either in hardware or software forms.
This rigid form of implementation which we refer as Hard Realization relies on
strict algorithmic accuracy constraints dictated to digital design engineers.
Hard realization admits paying as much as necessary implementation costs to
preserve computation precision and determinism throughout all the design and
implementation steps. Despite its prior accomplishments, this conventional
paradigm has encountered serious challenges with today's emerging applications
and implementation technologies. Unlike traditional hard computing, the
emerging soft and bio-inspired algorithms do not rely on fully precise and
deterministic computation. Moreover, the incoming nanotechnologies face
increasing reliability issues that prevent them from being efficiently
exploited in hard realization of applications. This article examines Soft
Realization, a novel bio-inspired approach to design and implementation of an
important category of applications noticing the internal brain structure. The
proposed paradigm mitigates major weaknesses of hard realization by (1)
alleviating incompatibilities with today's soft and bio-inspired algorithms
such as artificial neural networks, fuzzy systems, and human sense signal
processing applications, and (2) resolving the destructive inconsistency with
unreliable nanotechnologies. Our experimental results on a set of well-known
soft applications implemented using the proposed soft realization paradigm in
both reliable and unreliable technologies indicate that significant energy,
delay, and area savings can be obtained compared to the conventional
implementation.
