L1 - YORK UNIVERSITY CSE4210 CSE4210 Architecture and...

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1 YORK UNIVERSITY CSE4210 CSE4210 Architecture and Hardware for DSP Lecture 1 Introduction & Number systems YORK UNIVERSITY CSE4210 Administrative Stuff • CSE4210 Architecture and Hardware for DSP • Text: VLSI Digital Signal Processing Systems: Design and Implementation. K. Parhi. Wiley Interscience • Posted articles

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2 YORK UNIVERSITY CSE4210 Administrative Stuff • Office hours: Monday 1-2pm TR 3-4pm • Room 2026 CSEB x40607 •HW 0% • Quizes 10% • Midterm 25% • Projects 25% • Final 40% YORK UNIVERSITY CSE4210 Topics • Number systems • Fast arithmetic • Algorithm representation • Transformation (retiming, unfolding, folding) • Systolic arrays and mapping algorithms into hardware • Low power design
3 YORK UNIVERSITY CSE4210 Introduction • Introduction to DSP algorithms • Non-terminating programs in real time. • Speed depends on applications (audio, video, 2-D, 3-D, …) • Need to design families of architectures for specified algorithm complexity and speed constraints YORK UNIVERSITY CSE4210 Typical DSP Programs DSP System nT …3T 2T T 0 Input Output 3-Dimensional optimization: Area, Speed, Power) Usually, speed is a requirement, area-power tradeoff P=C V 2 F

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4 YORK UNIVERSITY CSE4210 Examples • FIR filter, x(n) is the input, y(n) output • IIR filter 1 0 () ()( ) J j y nh j x n j = = 10 ) ) Q P ik yn aiyn i bkxn k == =− +− ∑∑ YORK UNIVERSITY CSE4210 Examples • Convolution 11 00 ) ) MN ij xihn i h jxn j −− = −= For n=1 to M+N-2, y(n)=0, For i=0:M-1, y(n)=y(n)+x(i)*h(n-i) end end MAC operation
5 YORK UNIVERSITY CSE4210 More Complex Examples Motion Estimation • Image (frame) is divided into macroblocks • Each macroblock is compared to a macroblock in the reference frame using some error measure. • The search is conducted over a predetermined search area. • A vector denoting the displacement of the (motion) is sent. YORK UNIVERSITY CSE4210 More Complex Examples Motion Estimation • Many measures of errors could be used. • The displaced block difference s(m,n) using MAD (Mean Absolute Difference) is defined as ∑∑ + + = = = 1 0 1 0 ) , ( ) , ( ) , ( N i N j n j m i y j i x n m s m,n are in is the search area, N is the macroblock size.

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This note was uploaded on 02/13/2012 for the course CSE 4111 taught by Professor Edmonds during the Winter '12 term at York University.

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L1 - YORK UNIVERSITY CSE4210 CSE4210 Architecture and...

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