Amorphous Computing

Amorphous computing is the development of organizational principles and programming languages for obtaining coherent behaviors from the cooperation of myriads of unreliable parts that are interconnected in unknown, irregular, and time-varying ways. The impetus for amorphous computing comes from developments in microfabrication and fundamental biology, each of which is the basis of a kernel technology that makes it possible to build or grow huge numbers of almost-identical information-processing units at almost no cost. This paper sets out a research agenda for realizing the potential of amorphous computing and surveys some initial progress, both in programming and in fabrication. We describe some approaches to programming amorphous systems, which are inspired by metaphors from biology and physics. We also present the basic ideas of cellular computing, an approach to constructing digital-logic circuits within living cells by representing logic levels by concentrations DNA-binding proteins

Amorphous Computing

The goal of amorphous computing is to identify organizationalprinciples and create programming technologies for obtainingintentional, pre-specified behavior from the cooperation of myriadunreliable parts that are arranged in unknown, irregular, andtime-varying ways. The heightened relevance of amorphous computingtoday stems from the emergence of new technologies that could serve assubstrates for information processing systems of immense power atunprecedentedly low cost, if only we could master the challenge ofprogramming them. This document is a review of amorphous computing

Alternative approach to computing transport coefficients: application to conductivity and Hall coefficient of hydrogenated amorphous silicon

We introduce a theoretical framework for computing transport coefficients for complex materials. As a first example, we resolve long-standing inconsistencies between experiment and theory pertaining to the conductivity and Hall mobility for amorphous silicon and show that the Hall sign anomaly is a consequence of localized states. Next, we compute the AC conductivity of amorphous polyanaline. The formalism is applicable to complex materials involving defects and band-tail states originating from static topological disorder and extended states. The method may be readily integrated with current \textit{ab initio} methods.Comment: 4 pages, 2 figures, submitted to Phys. Rev. Let

An Algorithm for Group Formation and Maximal Independent Set in an Amorphous Computer

Amorphous computing is the study of programming ultra-scale computing environments of smart sensors and actuators cite{white-paper}. The individual elements are identical, asynchronous, randomly placed, embedded and communicate locally via wireless broadcast. Aggregating the processors into groups is a useful paradigm for programming an amorphous computer because groups can be used for specialization, increased robustness, and efficient resource allocation. This paper presents a new algorithm, called the clubs algorithm, for efficiently aggregating processors into groups in an amorphous computer, in time proportional to the local density of processors. The clubs algorithm is well-suited to the unique characteristics of an amorphous computer. In addition, the algorithm derives two properties from the physical embedding of the amorphous computer: an upper bound on the number of groups formed and a constant upper bound on the density of groups. The clubs algorithm can also be extended to find the maximal independent set (MIS) and $Delta + 1$ vertex coloring in an amorphous computer in $O(log N)$ rounds, where $N$ is the total number of elements and $Delta$ is the maximum degree

Paradigms for Structure in an Amorphous Computer

Recent developments in microfabrication and nanotechnology will enable the inexpensive manufacturing of massive numbers of tiny computing elements with sensors and actuators. New programming paradigms are required for obtaining organized and coherent behavior from the cooperation of large numbers of unreliable processing elements that are interconnected in unknown, irregular, and possibly time-varying ways. Amorphous computing is the study of developing and programming such ultrascale computing environments. This paper presents an approach to programming an amorphous computer by spontaneously organizing an unstructured collection of processing elements into cooperative groups and hierarchies. This paper introduces a structure called an AC Hierarchy, which logically organizes processors into groups at different levels of granularity. The AC hierarchy simplifies programming of an amorphous computer through new language abstractions, facilitates the design of efficient and robust algorithms, and simplifies the analysis of their performance. Several example applications are presented that greatly benefit from the AC hierarchy. This paper introduces three algorithms for constructing multiple levels of the hierarchy from an unstructured collection of processors

Performance Evaluation of DV-HOP and Amorphous Algorithms based on Localization Schemes in Wireless Sensor Networks

In the field of high-risk observation, the nodes in Wireless Sensor Network (WSN) are distributed randomly. The result from sensing becomes meaningless if it is not known from where the originating node is. Therefore, a sensor node positioning scheme, known as the localization scheme, is required. The localization scheme consists of distance estimation and position computing. Thus, this research used connectivity as distance estimation within range free algorithm DV-Hop and Amorphous, and then trilateral algorithm for computing the position. Besides that, distance estimation using the connectivity between nodes is not needed for the additional hardware ranging as required by a range-based localization scheme. In this research compared the localization algorithm based on range free localization, which are DV-Hop algorithm and Amorphous algorithm. The simulation result shows that the amorphous algorithm have achieved 13.60% and 24.538% lower than dv-hop algorithm for each parameter error localization and energy consumption. On node density variations, dv-hop algorithm gained a localization error that is 26.95% lower than amorphous algorithm, but for energy consumption parameter, amorphous gained 14.227% lower than dv-hop algorithm. In the communication range variation scenario, dv-hop algorithm gained a localization error that is50.282% lower than amorphous. However, for energy consumption parameter, amorphous algorithm gained 12.35%. lower than dv-hop algorithm

Programming an Amorphous Computational Medium

Amorphous computing considers the problem of controllingmillions of spatially distributed unreliable devices which communicateonly with nearby neighbors. To program such a system, we need a highleveldescription language for desired global behaviors, and a system tocompile such descriptions into locally executing code which robustly createsand maintains the desired global behavior. I survey existing amorphouscomputing primitives and give desiderata for a language describingcomputation on an amorphous computer. I then bring these together inAmorphous Medium Language, which computes on an amorphous computeras though it were a space-filling computational medium