Paintable Computing Project Fall 2003

Slides:



Advertisements
Similar presentations
Network II.5 simulator ..
Advertisements

A Graduate Course on Multimedia Technology 3. Multimedia Communication © Wolfgang Effelsberg Media Scaling and Media Filtering Definition of.
Programming Languages Marjan Sirjani 2 2. Language Design Issues Design to Run efficiently : early languages Easy to write correctly : new languages.
Paintable Computing. Die Cost Paintable ComputingStatus Hardware Distributed Programming Applications Hardware Reference Platform Process Self-assembly.
University College Cork IRELAND Hardware Concepts An understanding of computer hardware is a vital prerequisite for the study of operating systems.
CAD/CAM Design Process and the role of CAD. Design Process Engineering and manufacturing together form largest single economic activity of western civilization.
CS294-6 Reconfigurable Computing Day 3 September 1, 1998 Requirements for Computing Devices.
Basic Concepts The Unified Modeling Language (UML) SYSC System Analysis and Design.
Penn ESE534 Spring DeHon 1 ESE534: Computer Organization Day 9: February 24, 2014 Operator Sharing, Virtualization, Programmable Architectures.
Computer Science and Mathematical Basics Chap. 3 발표자 : 김정집.
Introduction Advantage of DSP: - Better signal quality & repeatable performance - Flexible  Easily modified (Software Base) - Handle more complex processing.
Marwan Al-Namari 1 Digital Representations. Bits and Bytes Devices can only be in one of two states 0 or 1, yes or no, on or off, … Bit: a unit of data.
Paintable Computer Ting Yan CS 851 Bio-Inspired Computing Presentation March 25, 2003.
Discovering Computers 2010
1. an electronic device that manipulates information, or "data“
1. 2 What is Digital Image Processing? The term image refers to a two-dimensional light intensity function f(x,y), where x and y denote spatial(plane)
Design Communication. Freehand Drawing Definition: The spontaneous representation of ideas on paper without the use of technical aids.
1 Introduction to Engineering Fall 2006 Lecture 17: Digital Tools 1.
CS434/534: Topics in Networked (Networking) Systems Network OS Abstraction: From Data to Function Store; Wireless Foundation: Frequency-Domain Analysis.
Computer Organization and Architecture Lecture 1 : Introduction
Recent Research in Sensate Media
3.3 Fundamentals of data representation
Tutor: Dr. Youssef Harrath
Programming paradigms
Fundamentals of Information Systems, Sixth Edition
3.3 Fundamentals of data representation
Scaling the Network: The Internet Protocol
Edexcel GCSE Computer Science Topic 15 - The Processor (CPU)
Principles of Information Technology
10.3 Details of Recursion.
CS140 Lecture 03: The Machinery of Computation: Combinational Logic
Architecture & Organization 1
Chapter 3 Top Level View of Computer Function and Interconnection
Paintable Computer Programming a
Invitation to Computer Science 5th Edition
Introduction to Computing Lecture # 1
Interfacing Memory Interfacing.
Introduction to cosynthesis Rabi Mahapatra CSCE617
Algorithm Design.
Lecture 41: Introduction to Reconfigurable Computing
Software Defined Networking (SDN)
Architecture & Organization 1
ECEG-3202 Computer Architecture and Organization
Objective of This Course
Computational Elements of Robust Civil Infrastructure
Programming Languages
Lesson 4 Synchronous Design Architectures: Data Path and High-level Synthesis (part two) Sept EE37E Adv. Digital Electronics.
ECEG-3202 Computer Architecture and Organization
MICROPROCESSOR MEMORY ORGANIZATION
Paintable Computing Project Summer 2004
Chapter 3 Hardware and software 1.
CSCI1600: Embedded and Real Time Software
Paintable Computing The goal of the paintable computing project is to jointly develop millimeter scale computing elements and techniques for self-assembly.
ECEG-3202 Computer Architecture and Organization
Image Transforms for Robust Coding
HIGH LEVEL SYNTHESIS.
Chapter 3 Hardware and software 1.
COMP60611 Fundamentals of Parallel and Distributed Systems
Bio-Inspired Computing
MA/CSSE 474 Theory of Computation
CSCI1600: Embedded and Real Time Software
ECE 352 Digital System Fundamentals
Scaling the Network: The Internet Protocol
Introduction to the Lab
An introduction to: Deep Learning aka or related to Deep Neural Networks Deep Structural Learning Deep Belief Networks etc,
William Stallings Computer Organization and Architecture 7th Edition
♪ Embedded System Design: Synthesizing Music Using Programmable Logic
William Stallings Computer Organization and Architecture 7th Edition
William Stallings Computer Organization and Architecture
Presentation transcript:

Paintable Computing Project Fall 2003 Virtual Self Assembly Turing substrate By way of introduction, I am the Capitan, Navigator, Oarsman, and Deck-swabbie on the paintable computing project here at MIT. And in my time with you, I would like to first put forth the notion of “Virtual Self-assembly”: - what it is - how far we’ve taken it - and why anyone should care. Summarizing it’s relevance, self-assembly in the virtual domain offers a promising avenue for overcoming the really gnawing complexity limitations of self-assembly in the physical domain. A dense mesh of spatially distributed locally interconnected Turing machines Virtual Self-assembly Spontaneous aggregation of spatially mobile fragments of code with state.

Paintable Computing Project Fall 2003 Turing Substrate - What Turing substrate 1) A dense irregular mesh of individually programmable Turing machines. 2) a 3D volume populated by logic-enabled nodes which are computationally universal. See also: computational fabric. Virtual self-assembly is predicated on -- and motivated by -- the notion of a “Turing substrate” . We use this name to describe a 3D volume populated my a dense irregular rmesh of logic-enabled nodes that are computationally universal This abstraction is a straightforward extension of work that models nature as an irregular mash of fine grain computing elements, each with task-specific physical hardware running a limited internalized procedure. For errant chip designers like myself, the notion of a Turing Substrate is simply an admission that today, you can fit a 32-bit Palm-class computer and a quarter meg of SRAM into 2 square mm of die.

Paintable Computing Project Fall 2003 Turing Substrate - Who Turing substrate 1) A dense irregular mesh of individually programmable Turing machines. 2) a 3D volume populated by logic-enabled nodes which are computationslly universal. See also: computational fabric. For a number of groups throughout the world, this is a core concept if not a term of art. Our work on paint specifically targets traditional silicon processing elements in sand-sized packages separated by millimeter length scales. For us, the vision is scoop out the dust, pour it into a paint-like goo, plaster it down, expose it to power, and it does the reset. A belief shared throughout this community has been that the hardware development was mostly evolutionary. But that the software was going to send us back to the drawing board. Smart Dust (U. C. Berkeley) Amorphous Computing (MIT LCS/AI-lab) Paintable Computing Project (CBA)

Paintable Computing Project Fall 2003 Virtual Self-assembly Virtual Self-assembly 1) The undirected reassembly of a process from randomly distributed fragments of code with state. 2) The spontaneous aggregation of a distributed process from mobile process fragments migrating within a computational medium. See also: Wack-o F1() Xn F1() Xn F1() Xn Xn Xn F1() F1()

Paintable Computing Project Fall 2003 Virtual Self-assembly Virtual Self-assembly 1) The undirected reassembly of a process from randomly distributed fragments of code with state. 2) The spontaneous aggregation of a distributed process from mobile process fragments migrating within a computational medium. See also: Wack-o F1() Xn F1() Xn F1() Xn Xn 1110010101 0110101010 1110010101 Xn F1() 1010010101 1110010101 1110010101 1110010101 0110101010 1001010100 F1() 1010100010 1110010101 1110010101

Paintable Computing Project Fall 2003 Virtual Self-assembly Virtual Self-assembly 1) The undirected reassembly of a process from randomly distributed fragments of code with state. 2) The spontaneous aggregation of a distributed process from mobile process fragments migrating within a computational medium. See also: Wack-o F1() Xn F1() Xn F1() 1110010101 Xn Xn 0110101010 1110010101 1010010101 Xn F1() 1110010101 1110010101 1110010101 0110101010 1001010100 F1() 1010100010 1110010101 1110010101

Paintable Computing Project Fall 2003 Virtual Self-assembly Virtual Self-assembly 1) The undirected reassembly of a process from randomly distributed fragments of code with state. 2) The spontaneous aggregation of a distributed process from mobile process fragments migrating within a computational medium. See also: Wack-o F1() Xn F1() F1() Xn F1() Xn Xn Xn F1() F1()

Paintable Computing Project Fall 2003 Tessellation via Thermodynamic (-like) relaxation

Paintable Computing Project Fall 2003 Distributed Graphics Controller

Early Applications Media Streaming Holistic Data Storage Surface Bus Some other applications we have demonstrated in simulation: Streaming Media -- where packetized media is modeled as a diffusive “data gas” that is streamed through a single port, randomizes its position for storage, and re-assembles itself into a serial order for output Holistic Data Storage builds on this idea by adding signal processing to construct holistic representations for image storage – here, the data gas reacts enzymatically with pfrags that perform space-frequency transformation on the packets. The result that undersampling of the packets results in reconstructed images that are undersampled in spectrum -- much like a hologram. In Surface Bus, objects placed on the periphery of a 2D surface diffuse into the 2D ensemble small process fragments that replicate and self organize into a dynamic token ring. And finally, Image Segmentation is realized as the competition among process fragments, each selective for image characteristics such as sky, water, trees, and sand Holistic Data Storage Surface Bus Image Segmentation

Paintable Computing Project Fall 2003 Why Virtual Self-assembly? = ? Turing Substrate + Sensing/Actuation Universal Self-assembler Beyond individual applications lurks the real questions of how this technique relates to the fundamentals of a budding field. I see three points worth raising: 1) The first, a question. Does a threshold number of universal computers, arranged in a dense irregular mesh, and each with some capacity to sense and actuate the physical world, beget an universal assembler. I am totally out of my water make or refute the claim. But the question begs an answer. And I am grateful for anyone’s input. 2) Back on the shop floor, The combination of a Turing substrate whose nodes are stamped out by millions in mature wafer foundries, together with mobile code whose replication machinery is a simple digital copy, seems an interesting alternative to von-Neuman’s original abstraction of machine of potentially unbounded complexity replicating itself. Copy

Paintable Computing Project Fall 2003 Why Virtual Self-assembly? Defect likelihood as limitation to growth Stoichiometric limits on component placement 3) Finally, virtually self-assembly holds out the promise of overcoming the gnawing problems of contemporary techniques for self-assembly. will likely be most profitably used as an adjunct to material self-assembly. On deck for my work is a iterative sequencing of material and virtual self-assembly. With material resources assembling into coarse aggregates followed by detailed functional assignment in the virtual domain. At which point the sequence repeats using the previous result as a dynamic scaffold. Virtual Self-assembly Stage 2 Stage 4 Material Self-assembly Stage 1 Stage 3

Paintable Computing Project Fall 2003 Title F1()