What are the alternatives to silicon-based digital computing? What problems will future computers be able to solve that contemporary machines cannot or may never be able to solve? What are the physical substrates underlying these future computers, and what are the problems preventing their implementation now? These are some of the questions that motivate this course. We will review contemporary computing technology and examine its theoretical foundations as well as its fundamental limitations. We will then focus on three proposed alternatives: neural computing, DNA computing, and quantum computing. To get a better grasp of these three technologies, we will experiment with DNA and quantum computer simulators, in addition to various types of artificial neural networks. Students will be evaluated based on homework assignments and a research mini-project, involving a written report and a brief oral presentation at the end of the quarter.

• Course Details:
• Goals and syllabus
• Meeting Times and Due Dates
• Lectures: Thursdays, 6:30-9:20pm, in EE1 003
• Homework Assignments: Due Thursdays, at the beginning of class, in EE1 003
• Research Mini-Project: Oral presentation on March 8; Report due March 12-15
• Midterm/Final Exam: None
• Holidays: None
• Academic accomodations
• Workload and grading
• Policies on collaboration and cheating
• Announcements and e-mail addresses
• Overall schedule of lecture topics
• Instructor: Rajesh Rao (rao@cs)
• Office hours: By appointment (send e-mail). Location: 325D Sieg.
• TA: Aaron Shon (aaron@cs)
• Office hours: By appointment
• Weekly Homework Assignments
• Research MiniProject
• Lecture Slides: Online versions of the slides used in lectures.
• Handouts and Reading List
• Online Resources: Tutorials, Articles, and Software
• Class E-mail Archive: Messages sent to cse599@cs.washington.edu.
• Textbook: The Feynman Lectures On Computation, edited by Anthony J. G. Hey and Robin W. Allen, Addison-Wesley, 1996. Available at the UW campus bookstore, and at any of the online booksellers.
• Other Useful Books and References:
  • A. K. Dewdney, The Turing Omnibus, Computer Science Press, 1989.
  • Anthony J. G. Hey (ed), Feynman and Computation: Exploring the Limits of Computers, Perseus Books, 1999.
  • Michael A. Arbib (ed), The Handbook of Brain Theory and Neural Networks, MIT Press, 1995.
• Feedback: Tell us what you think about the class (anonymously, if you desire).

Please keep in mind that this document is not static, and that new information (especially class announcements and messages) will be added frequently.