Departmentof Electrical and Computer Engineering
University of Wisconsin - Madison
ECE 734 VLSI Array Processors for Digital Signal Processing


Spring Semester 2020

Time and Place: Lec. 1, 9:30 - 10:45 AM, TTh, 3534 Engr. Hall
Instructors: Yu Hen Hu, 3625 Engr. Hall, Tel. 262-6724, E-mail: yhhu AT wisc dot edu
Prerequisite: ECE 431 Digital Signal Processing or equivalent, ECE/CS 552 Computer Architecture, or equivalent. General knowledge of numerical linear algebra, signal processing algorithms, and digital system design.
Goals: This course presents design methodologies of computing, communication, signal processing and machine learning algorithms over embedded micro-architecture and platforms. The emphasis is on cross-layer joint optimization of application algorithm design and implementation hardware/software co-design space exploration. Current focus are on algorithmic level design methodologies, with applications to the implementations of deep neural networks, 5G communication algorithms, embedded computer vision and multimedia (video/image coding) algorithms.
  • Design and Implementations of signal processing and machine learning algorithms
  • Review of numerical linear algebra algorithms
  • Review of signal processing algorithms: linear transformations, and digital filters
  • High level synthesis, dependence graph, binding and scheduling
  • Parallel algorithm transformation: Pipelining and vector pocessing, loop transformation
  • Recurrent algorithm transformation: iteration bounds, retiming, folding, unfoling, look-ahead transform
  • Systolic array processing
  • Implementation of Deep Neural Networks
  • Implementation of 5G wireless algorithms
  • Implementation of video coding algorithms
  • Implementation of computer vision algorithms
Textbook: There will be no official textbook. here is a list of reference books (to be updated). Additional lecture notes will be post on line.
Computer Usage: Matlab may be used to demonstrate some algorithms. Students who work on specific class projects may need to use other software.
Homework: Two to three homework assignments will be given. In addition to problems, homework may involve hand-coding of algorithms
Grading Policy: (tentative) 15% Homework assignments
15% Paper reading and presentation
50% individual class project, proposal, presentation, reports
20% take-home final exam

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Last Modified: January 20, 2020