scholarly journals A Simplicity Criterion for Physical Computation

2019 ◽  
Vol 70 (1) ◽  
pp. 153-178 ◽  
Author(s):  
Tyler Millhouse
Keyword(s):  
Physical Computation

scholarly journals Morphological Computation: Nothing but Physical Computation

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 942 ◽  
Author(s):  
Marcin Miłkowski
Keyword(s):  
Energy Efficiency ◽  
Physical Computation ◽  
Efficiency Cost ◽  
Morphological Computation ◽  
Computational Systems

The purpose of this paper is to argue against the claim that morphological computation is substantially different from other kinds of physical computation. I show that some (but not all) purported cases of morphological computation do not count as specifically computational, and that those that do are solely physical computational systems. These latter cases are not, however, specific enough: all computational systems, not only morphological ones, may (and sometimes should) be studied in various ways, including their energy efficiency, cost, reliability, and durability. Second, I critically analyze the notion of “offloading” computation to the morphology of an agent or robot, by showing that, literally, computation is sometimes not offloaded but simply avoided. Third, I point out that while the morphology of any agent is indicative of the environment that it is adapted to, or informative about that environment, it does not follow that every agent has access to its morphology as the model of its environment.

scholarly journals Solving matrix equations in one step with cross-point resistive arrays

2019 ◽  
Vol 116 (10) ◽  
pp. 4123-4128 ◽  
Author(s):  
Zhong Sun ◽  
Giacomo Pedretti ◽  
Elia Ambrosi ◽  
Alessandro Bricalli ◽  
Wei Wang ◽  
...  
Keyword(s):  
Data Storage ◽  
Linear Equations ◽  
Single Step ◽  
Feedback Connection ◽  
Physical Computation ◽  
Cross Point ◽  
Physical Computing ◽  
The Matrix ◽  
One Step ◽  
Classical Computing

Conventional digital computers can execute advanced operations by a sequence of elementary Boolean functions of 2 or more bits. As a result, complicated tasks such as solving a linear system or solving a differential equation require a large number of computing steps and an extensive use of memory units to store individual bits. To accelerate the execution of such advanced tasks, in-memory computing with resistive memories provides a promising avenue, thanks to analog data storage and physical computation in the memory. Here, we show that a cross-point array of resistive memory devices can directly solve a system of linear equations, or find the matrix eigenvectors. These operations are completed in just one single step, thanks to the physical computing with Ohm’s and Kirchhoff’s laws, and thanks to the negative feedback connection in the cross-point circuit. Algebraic problems are demonstrated in hardware and applied to classical computing tasks, such as ranking webpages and solving the Schrödinger equation in one step.

Designing Architectural Morphing Skins with Elastic Modular Systems

2011 ◽  
Vol 9 (4) ◽  
pp. 397-419 ◽  
Author(s):  
Chin Koi Khoo ◽  
Flora Salim ◽  
Jane Burry
Keyword(s):  
Climatic Conditions ◽  
Existing Buildings ◽  
Physical Computation ◽  
Modular Systems ◽  
Mechanical Joints ◽  
Kinetic Transformation ◽  
Elastic Form ◽  
Shading Devices ◽  
Design Experiments

This paper discusses the issues of designing architectural skins that can be physically morphed to adapt to changing needs. To achieve this architectural vision, designers have focused on developing mechanical joints, components, and systems for actuation and kinetic transformation. However, the unexplored approach of using lightweight elastic form-changing materials provides an opportunity for designing responsive architectural skins and skeletons with fewer mechanical operations. This research aims to develop elastic modular systems that can be applied as a second skin or brise-soleil to existing buildings. The use of the second skin has the potential to allow existing buildings to perform better in various climatic conditions and to provide a visually compelling skin. This approach is evaluated through three design experiments with prototypes, namely Tent, Curtain and Blind, to serve two fundamental purposes: Comfort and Communication. These experimental prototypes explore the use of digital and physical computation embedded in form-changing materials to design architectural morphing skins that manipulate sunlight and act as responsive shading devices.

scholarly journals Physical Computation as Dynamics of Form that Glues Everything Together

Information ◽  
2012 ◽  
Vol 3 (2) ◽  
pp. 204-218 ◽  
Author(s):  
Gordana Dodig Crnkovic
Keyword(s):  
Physical Computation

scholarly journals The Geometry of Speed Limiting Resources in Physical Models of Computation

2017 ◽  
Vol 28 (04) ◽  
pp. 321-333 ◽  
Author(s):  
Benjamin Russell ◽  
Susan Stepney
Keyword(s):  
Limited Resource ◽  
Minimum Time ◽  
Physical Models ◽  
Maximum Speed ◽  
Speed Limit ◽  
Physical Computation ◽  
Speed Limits ◽  
Models Of Computation ◽  
Special Cases ◽  
Time Optimal

We study the maximum speed of quantum computation and how it is affected by limitations on physical resources. We show how the resulting concepts generalize to a broader class of physical models of computation within dynamical systems and introduce a specific algebraic structure representing these speed limits. We derive a family of quantum speed limit results in resource-constrained quantum systems with pure states and a finite dimensional state space, by using a geometric method based on right invariant action functionals on [Formula: see text]. We show that when the action functional is bi-invariant, the minimum time for implementing any quantum gate using a potentially time-dependent Hamiltonian is equal to the minimum time when using a constant Hamiltonian, thus constant Hamiltonians are time optimal for these constraints. We give an explicit formula for the time in these cases, in terms of the resource constraint. We show how our method produces a rich family of speed limit results, of which the generalized Margolus–Levitin theorem and the Mandelstam–Tamm inequality are special cases. We discuss the broader context of geometric approaches to speed limits in physical computation, including the way geometric approaches to quantum speed limits are a model for physical speed limits to computation arising from a limited resource.

Heterogeneous and Elastic Computation Framework for Mobile Cloud Computing

2014 ◽  
Vol 24 (07) ◽  
pp. 1013-1037 ◽  
Author(s):  
Chi-Sheng Shih ◽  
Joen Chen ◽  
Yu-Hsin Wang ◽  
Norman Chang
Keyword(s):  
Mobile Devices ◽  
Mobile Applications ◽  
Mobile Cloud Computing ◽  
Network Capacity ◽  
Physical Computation ◽  
Imprecise Computation ◽  
Cloud Servers ◽  
Real Time Computing ◽  
And Storage ◽  
Computing Literature

The number and variety of applications for mobile devices continue to grow. However, the resources on mobile devices including computation and storage do not keep pace with the growth. How to incorporate the computation capacity on cloud servers into mobile computing has been desired and challenge issues to resolve. In this work, we design an elastic computation framework to take advantage the heterogeneous computation capacity on cloud servers, which consist of CPUs and GPGPUs, to meet the computation demands of ever growing mobile applications. The computation framework extends OpenCL framework to link remote processors with local mobile applications. The framework is flexible in the sense that the computation can be stopped at any time and gains results, which is called imprecise computation in real-time computing literature. The framework has been evaluated against OpenCL benchmark and physical computation engine for gaming. The results show that the framework supports OpenCL benchmark, RODINIA, without modifying the codes with few exceptions. The elastic computation framework allows the cloud servers to support more mobile clients without sacrificing their QoS requirements. The experiment results also show that IO intensive applications do not perform well when the network capacity is insufficient or unreliable.

scholarly journals Physical Computation: A Mechanistic Account

2018 ◽  
Vol 127 (3) ◽  
pp. 426-431
Author(s):  
Alistair M. C. Isaac
Keyword(s):  
Physical Computation

scholarly journals Morphological, Natural, Analog, and Other Unconventional Forms of Computing for Cognition and Intelligence

Proceedings ◽  
2020 ◽  
Vol 47 (1) ◽  
pp. 30
Author(s):  
Gordana Dodig-Crnkovic
Keyword(s):  
Cognitive Science ◽  
Computational Models ◽  
The Body ◽  
Knowledge Generation ◽  
Symbol Manipulation ◽  
Physical Computation ◽  
Natural Analog ◽  
Morphological Computation ◽  
Living Organisms ◽  
High Level

According to the currently dominant view, cognitive science is a study of mind and intelligence focused on computational models of knowledge in humans. It is described in terms of symbol manipulation over formal language. This approach is connected with a variety of unsolvable problems, as pointed out by Thagard. In this paper, I argue that the main reason for the inadequacy of the traditional view of cognition is that it detaches the body of a human as the cognizing agent from the higher-level abstract knowledge generation. It neglects the dynamical aspects of cognitive processes, emotions, consciousness, and social aspects of cognition. It is also uninterested in other cognizing agents such as other living beings or intelligent machines. Contrary to the traditional computationalism in cognitive science, the morphological computation approach offers a framework that connects low-level with high-level approaches to cognition, capable of meeting challenges listed by Thagard. To establish this connection, morphological computation generalizes the idea of computation from symbol manipulation to natural/physical computation and the idea of cognition from the exclusively human capacity to the capacity of all goal-directed adaptive self-reflective systems, living organisms as well as robots. Cognition is modeled as a layered process, where at the lowest level, systems acquire data from the environment, which in combination with the already stored data in the morphology of an agent, presents the basis for further structuring and self-organization of data into information and knowledge.

Sign in / Sign up

Export Citation Format

Share Document