COPYRIGHT NOTICE This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.     System Level Design of DSP Architectures Master level No. of credits: 8  Textbooks:  D. Gajski, S. Abdi, A. Gerstlauer, G. Schirner, Embedded Systems Design, Springer, 2009 K.K. Parhi, VLSI Digital Signal Processing Systems: Design and Implementation, Wiley, 1999  Tutorials: System level design flow: what is needed and what is not (D. Gajski, University of California, Irvine ) ESL - A methodology for handling complexity (B. Bailey, G. Martin, A. Piziali) High-level synthesis: past, present, and future (G. Martin, G. Smith) | A platform-based taxonomy for ESL design (D. Densmore, A. Sangiovanni-Vincetelli, R. Passerone) General description The course focuses on design principles and software tools related to system level design of complex DSP architectures. Hardware implementations solutions for overall power minimization and faster operating frequency are also outlined, related to pipelining and algorithm transformation (retiming, folding, unfolding). Case studies include adaptive filtering, multiresolution analysis, and oversampling data converters.  Course outline  Lecture 1:  More info: 1 | 2 | 3 Overview of system level design principles. Taxonomy of Electronic System Level (ESL) tools.  Lecture 2:  More info: 1 | 2 Finite precision effects in DSP algorithms implementation.  Homework 1: Adaptive channel equalization using the LMS algorithm. theoretical background | homework | M_files  Lecture 3:  More info: 1 | 2 | 3 System design methodologies and terminology.  Lecture 4:  More info: 1 | 2 Models of computation. Synchronous data flow models. Control flow models.  Lecture 5:  More info: 1 | 2 | 3 Algorithmic transformations. Pipelining. Retiming.  Homework 2 : Face recognition using PCA algorithm. theoretical background | homework | M_files  Lecture 6:  More info: 1 | 2 Algorithmic transformations. Unfolding. Folding.  Lecture 7:    Lecture 8:    Lecture 9:    Lecture 10:    Lecture 11:    Lecture 12:    Lecture 13:    Lecture 14: Course review. Q&A.