CSE 599 Syllabus

Catalog Data

Description: Examines the future of computers. Considers the fundamental limitations of contemporary silicon-based computing technology. Focuses on three proposed alternatives: neural computing, DNA computing, and quantum computing.

Credits: 4

Introduction

In 1965, Intel co-founder Gordon Moore observed that the number of components that could be made to fit on a chip doubles every two years, thereby doubling the computing power available for a given cost. This observation, known as Moore's law, has continued to hold true: fueled by continuous improvements in integrated-circuit technology, the throughput of digital computers has increased at an exponential rate in the past few decades. But the writing is now clearly on the wall: Moore's law cannot hold forever. Electronic circuits can only get so small. In about 10 years, the components on a chip will only be a few atoms apart. What lies beyond Moore's law?

In this course, we will examine alternatives to silicon-based digital computing. We will start by considering present-day IC technology, its theoretical foundations, and its limitations. We will then examine some of the proposed alternatives to sequential digital machines: neural computing, quantum computing, and DNA computing.

Our goal will be to develop the requisite background in these subjects, and then to understand the real limitations and promise of each technology.

Course Syllabus

      1.  Introduction:  History of computing: Who, what, and when
                Theoretical Foundations: Automata and Turing machines
                Computability, the halting problem, and undecidability
                Computational complexity: P versus NP, PSPACE
                Analog versus Digital computing
      2.  Digital Computer Organization
                Gates and combinationial logic
                Flip-flops and shift registers
                Transistors and integrated circuits
                Silicon-technology scaling: the physical and technological limitations
      3. DNA computing
                DNA, RNA, protein synthesis, base pairs, enzymes
                Computational premise: Mapping sequential computations to DNA, self-assembly
                Example: Solving an NP-complete problem
                Technology: Errors and correction, beyond toy problems
      4. Introduction to Neurobiology
                Neurons, dendrites, axons, synapses
                Signals and signaling in the nervous system
                Computation in nervous tissue: Local learning, LTP, LTD, development, growth
                Neural circuits and their role in perception and action
      5. Neural Computing
                Distributed representations in neural networks
                Feedforward versus recurrent networks, hierarchical networks
                Supervised learning: Delta rule, Backpropagation
                Unsupervised learning: Hebbian learning, competitive learning, self-organizing maps
                Spike-based computing and learning
      6.  Information Theory, Thermodynamics, and Reversible Computing
                What is information?
                Communicating information: Noise, channel capacity, signaling
                Error correction coding and computation
                The laws of thermodynamics
                Noise, entropy, reversible computing
      7. Quantum computing
                Spin, phase, states, superposition, Dirac notation
                Computing with a superposition of quantum states
                Example: Factoring integers (Shor's algorithm)
                Reversible logic gates, the CONTROL-NOT gate
                Technology: ion traps and NMR, decoherence, error correction

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