Future Computers

CSCI 107, Spring 2010

When Moore’s law runs out of room

• When transistors become only tens of atoms thick – Quantum mechanics applies– Defects are harder to control– Heat is extreme

• “Dual-core” chips avoid these issues

What’s next?

• Alternative architectures and nanomaterials

• Perfecting new ways to process information– E.g., quantum computing and biological

computing

New Architectures-Memristor • Smallest transistors are 32 nanometers

wide—about 96 silicon atoms across • crossbar approach has parallel

nanowires in one plane crossing over a set of wires at right angles

• A 1 molecule thick buffer layer is between them

• The intersections between the two sets of wires act like switches, called memristors

• They represent 1s and 0s as transistors do, but also store more information.

• 1 memristor can do the work of 10 or 15 transistors.

Multiple Cores• When clock cycles reached 3 to 4

GHz chips reached the heat ceiling • For greater performance, designers

placed two processors on 1 chip• Personal computers now have

quadruple cores– Intel i7 – AMD Phenom X4

• Need to create languages and tools for software developers of consumer applications– Microsoft’s F# programming language – More needed

Faster Transistors

• researchers hope to make graphene transistors– 10 nm across and one atom high – Faster than field-effect transistors. – Lose very little energy from scattering or colliding

with atoms in the lattice, so less heat is generated

Different Computing Schemes

• Current Efforts– Optical– Biological– Quantum

• Criteria for being a computer– Represent information – Operate on that data

• Turing machine

Optical Computing

• Representing information– photons carry information, not electrons, and

they do so at the speed of light• Computation

– Controlling light is much more difficult – Current work: optical switches and optical

interconnect between traditional processors

DNA Computing• Representing data and instructions

– DNA molecules– Theses molecules store the “programming” that

directs the lives of our cells

DNA Computing• Computing Tools

– Watson-Crick pairing• every strand of DNA has its Watson-Crick complement

– Polymerases• copy information from one molecule into another

– Ligases• binds molecules together

– Nucleases• cut nucleic acids

– Gel electrophoresis• A solution of heterogeneous DNA molecules is placed in one

end of a slab of gel, and a current is applied– DNA synthesis

• write a DNA sequence on a piece of paper, send it to a commercial synthesis

• Massively parallel, energy efficient, clean

Quantum Computing• Representing Data

– The energy state of a hydrogen atom

• An atom in its ground state, with its electron in its lowest possible energy level can represent a 0

• The atom in an excited state, with its electron at a higher energy level can represent a 1

Representing Information

• Quantum computers aren't limited to two states

• Quantum bits, or qubits, can exist in superposition– when checked, the qubit will read 1 half of the

time and 0 half of the time• Quantum Physics

Quantum Computing

• Qubits can be set and read using lasers to pulse energy

• Operations:– AND, NOT, COPY

• Big Problem: How to isolate atoms:– Ion traps use optical and/or magnetic fields– Optical traps use light waves to trap and control

particles. – Quantum dots are made of semiconductor material

and are used to contain and manipulate electrons.

Quantum Parallelism• Quantum entanglement

– if you apply a force to 2 atoms in superposition, they can become entangled

– In entanglement the original information no longer resides in a single quantum bit but is stored instead in the correlations between qubits

– Measuring one bit, thereby putting it in a definite state, causes the other bit to also enter a definite state

• “Quantum Parallelism”---massively parallel, non-deterministic computing– Put all the input bits in equal superposition of 0 and 1---an equal

superposition of all possible inputs. – Run this input through a logic circuit that carries out a particular

computation. – The result is a superposition of all the possible outputs of that

computation.

Benefits

• Clean, fast, and can solve a new class of problems