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Physical Computing = Computing over real-value quantities,
typically experienced in the physical world.
Physical Computing includes analog, optical, neuromorphic, and quantum computing approaches.
One can formulate an equivalance between one real-valued computing medium to
another real-valued computing medium.
Although digital computing has a complete framework from high-level theory (e.g. Turing Machine)
through physical implementation,
classically Analog design has lacked an equivalent computing framework.
Recent programmable and configurable (e.g. FPAA) analog and mixed-signal capabilities,
have motivated and inspired building a parallel computational framework for
analog and mixed-signal approaches.
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First discussion on analog Computation Framework
(.pdf) [1].
You can watch the presentation from ICRC 2016 conference at this
link .
This presentation was the first talk given at the
first rebooting computing conference (ICRC).
Analog Numerical Analysis
( .pdf ) [2]:
noise and computational comparison of numerical algorithms.
In general, digital has higher starting resolution,
where analog has better computational numerics given that starting resolution.
Some algorithms are better in digital and some algorithms are better in analog.
Digital is not pristine and analog is noisy.
Both have their tradeoffs, and in many places, analog is numerically superior.
Analog Abstraction
( Abstraction [3].
Analog Architectures
( Complexity ) [4].
Impact of the Analog Numerical Analysis,
Analog Abstraction,
and Architectures towards mixed-signal computation
[5]
[6]
Unification of Physical Computation:
Analog, Optical, Neuromorphic, and Quantum computing approaches over real-valued variables.
[7]
[8]
References :
[1] J. Hasler,
“Opportunities in Physical Computing driven by Analog Realization,”
IEEE IC Rebooting Computing, San Diego, October 2016.
[2]
J. Hasler,
"Starting Framework for Analog Numerical Analysis for Energy Efficient Computing",
Journal of Low Power Electronics Applications,
vol. 7, no. 17, June 2017. pp. 1-22.
[3]
J. Hasler, S. Kim, and A. Natarajan,
“Enabling Energy-Efficient Physical Computing through Analog Abstraction and IP Reuse,”
Journal of Low Power Electronics Applications,
published December 2018.
pp. 1-23.
[4] J. Hasler,
“Analog Architecture Complexity Theory Empowering Ultra-Low Power
Configurable Analog and Mixed Mode SoC Systems,”
Journal of Low Power Electronics Applications, Jan. 2019. pp. 1-37.
[5] J. Hasler,
"Abstraction, IP Reuse, and Algorithmic Framework for Physical Computing,"
GOMAC, March 2018.
[6] J. Hasler,
“Analog Abstraction, Computation, and Numerical Analysis,”
IEEE ISCAS, Florance, Italy, May 2018.
[7] E. Black and J. Hasler,
“A Model and Implication of Real-Valued Physical Computing,”
GOMAC 2020.
[8] J. Hasler and E. Black,
"Physical Computing: Unifying Real Number Computation,"
Invited submission in IEEE Procedings, in review.
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