Slow Computing - Revision history

Murray: /* And There's Much More */

2010-06-03T20:39:13Z

And There's Much More

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	=== And There's Much More ===		=== And There's Much More ===
	~~Bacterial chemotaxis is just one of many interesting processes implemented using biomolecular feedback systems.~~

	=== References and Resources ===		=== References and Resources ===

Murray: /* Challenge */

2010-06-03T20:38:55Z

Challenge

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	A candidate architecture for implementation of such a system is shown below:		A candidate architecture for implementation of such a system is shown below:
	<center>		<center>
	[[~~Media~~:slowncs-arch.png]]		[[Image:slowncs-arch.png]]
	</center>		</center>
	The system consists of a set of agents that are interconnected through		The system consists of a set of agents that are interconnected through

Murray: /* Challenge */

2010-06-03T20:38:28Z

Challenge

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 time delay, asynchronous execution of parallel computations and highly
 complex interconnections play a defining role.
 === And There's Much More ===

Murray: /* Context */

2010-06-03T20:31:06Z

Context

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 deg/sec~\cite{BD06-JEB} and it can fly in wind gusts that are up to
 X its flight speed in air~\cite{FSDM09-inprep}.
 === And There's Much More ===

Murray: /* Context */

2010-06-03T20:29:57Z

Context

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	=== Context ===		=== Context ===

			Current approaches to design of software-controlled systems make use
			of a combination of abstractions and design techniques that are often
			implicitly based on the assumption that significant computational
			capacity is available to implement computations and communications.
			This is a good assumption for many application areas where substantial
			amounts of computing can be embedded within a physical system to
			control the dynamical behavior of the underlying process. As a
			consequence, many of the approaches that are available for designing
			complex, cyberphysical systems rely on large amounts of computing to
			achieve complex and robust behavior.

			As a complementary approach, consider instead the control system for a
			fruit fly, depicted in the figure at the top of the page.
			This system uses approximately 300,000 neurons with typical time
			constants in the range of 1--100 msec (10-1000 Hz) and has a physical
			size approximately equal to that of a sesame
			seed~\cite{mjd09-private}. Yet it is
			able to take off, land, avoid obstacles, find food and mate (among
			other things), often with performance that is beyond what we can do in
			engineered systems at this size scale. As just two specific instances, the
			control system of a fly is capable of executing saccades (rapid
			changes in direction) that occur at angular rates of up to 1800
			deg/sec~\cite{BD06-JEB} and it can fly in wind gusts that are up to
			2X its flight speed in air~\cite{FSDM09-inprep}.

	=== And There's Much More ===		=== And There's Much More ===

Murray at 20:27, 3 June 2010

2010-06-03T20:27:11Z

← Older revision		Revision as of 20:27, 3 June 2010
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	be seen through the broad usage of feedback controllers in modern		be seen through the broad usage of feedback controllers in modern
	application areas, ranging from transportation to communications to		application areas, ranging from transportation to communications to
	medicine to robotics. The goal of this ~~challenges~~ lies at the other end		medicine to robotics. The goal of this challenge lies at the other end
	of the computational spectrum: can we develop new principles and		of the computational spectrum: can we develop new principles and
	tools for the design of closed loop control systems using highly		tools for the design of closed loop control systems using highly
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	\|		\|
	<center>		<center>
	[[Image:flyflight-gust.png]]		<!-- [[Image:flyflight-gust.png]] -->
			[[Image:flyflight-circuitry.png]]
	[[Image:flyflight-block.png]]		[[Image:flyflight-block.png]]
	</center>		</center>

Murray: Created page with 'This page is being written up for a report on the “Impact of Control,” being edited by Tariq Samad and Anuradha Annaswamy. {| width=100% |- valign=top | bgcolor=lightyellow …'

2010-06-03T19:28:45Z

Created page with 'This page is being written up for a report on the “Impact of Control,” being edited by Tariq Samad and Anuradha Annaswamy. {| width=100% |- valign=top | bgcolor=lightyellow …'

New page

This page is being written up for a report on the “Impact of Control,” being edited by Tariq Samad and Anuradha Annaswamy.

{| width=100%
|- valign=top
| bgcolor=lightyellow |
Current techniques for the design of software-enabled control systems
rely on the existence of high performance sensing, actuation and
computational devices that can be embedded within a physical system at
modest cost. Driven by Moore's law, the success of this paradigm can
be seen through the broad usage of feedback controllers in modern
application areas, ranging from transportation to communications to
medicine to robotics. The goal of this challenges lies at the other end
of the computational spectrum: can we develop new principles and
tools for the design of closed loop control systems using highly
distributed, but ''slow'', computational elements.

The motivation for control design using slow computing is to develop
new architectures for feedback control systems that can be used in
applications where computational power is extremely limited. One
important class of such systems is that for which the energy usage of
the system must remain small, either due to the source of power
available (e.g. batteries or solar cells) or the physical size of the
device (e.g. microscale and nanoscale robots). A longer term
application area is in the design of control systems using novel
computing substrates, such as biological circuits. A critical element
in both cases is the tight coupling between the dynamics of the
underlying process and the temporal properties of the algorithm that is
controlling it.

Design of feedback systems using slow computing is particularly
challenging due to the performance limitations placed on systems with
computational delays that are comparable to the underlying dynamics.
These systems
are likely to use highly parallel, non-deterministic architectures to
achieve what is normally accomplished through the tightly synchronized,
serial interconnections of sensing, filtering, estimation, planning,
regulation and actuation that are common in traditional control
systems. Unfortunately, current techniques for systematic design of
control systems assume a mostly serial processing architecture and
techniques that make use of parallel architectures (such as neural
networks) do not provide sufficiently systematic design methods. New
research is required to develop the architectures, theory and tools
required to design controllers where computational delay does
not allow current techniques to be utilized.
|
<center>
[[Image:flyflight-gust.png]]
[[Image:flyflight-block.png]]
</center>
|}

=== Context ===

=== And There's Much More ===
Bacterial chemotaxis is just one of many interesting processes implemented using biomolecular feedback systems.

=== References and Resources ===