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**Programmable FIR Filter Design**

EE577b Fall2001 Final Project Programmable FIR Filter Design Draft #1 (10/25/2001) Prof. Peter A. Beerel

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**What is FIR Filter? FIR (Finite Impulse Response) Filter**

A generalization of the moving window on the flow of data Let the number of samples in the window be N Weighted sum of last N samples Weights given by N coefficients Output zero after N successive samples of zero input

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**FIR Filter Mathematical Representation**

It is convolution between input data and coefficients. Linear phase-shift FIR filter FIR filters with a symmetric set of coefficients Benefits of Linear phase-shift FIR filter The phase shift goes linearly with frequency In typical low-pass filters, overall distortion is limited Disadvantage of FIR filters Major disadvantage of FIR filter is that the width of window is larger than IIR filter for similar frequency response

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**Trojan FIR Filter Spec (Async)**

x0..x8,y0..y8 Dual-Rail Channels Signed magnitude fix-point representation x8, y8: sign x7, x6, y7, y6 integer field x0, x1, x2, x3, x4, x5, y0, y1, y2, y3, y4, y5 fraction field First 7 y outputs of each packet are garbage cmd 1’b01: read 8 coefficient on x, h0 through h7 1’b10: read packet of inputs [32 samples] & run Top Diagram

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**Trojan FIR Filter Spec (Sync)**

X[8:0], Y[8:0] Signed magnitude fix-point representation [8]: sign [7,6] integer field [5,0] fractional field First 7 y outputs of each packet are garbage Cmd (L and K user-defined) 1’b01: read 8 coeffs on x h0 through h7 Each input sample holds for L clocks 1’b10: read packet of inputs [32 samples] Each input sample holds for K clocks 1’b11: idle Top Diagram

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**Signed-Magnitude Fixed-Point Representation**

x = (-1)x[8]*(x[7]21+x[6] 20+x[5]2-1+…x[0]2-5) = (-1)*( )b=( )d Fixed-Point: Can maintain good precision with adequate rounding scheme Much simpler hardware than Floating-Point Signed-Magnitude Can use unsigned multiplier Adder (Accumulator) will be more complex Switching activity for synchronous single-rail design is smaller (-1 to 1) 2’s complement : to (7 bits flip) Signed-magnitude: to (1 bit flips) Quantitative study shows that many subsequent values are changing around zero Attractive characteristic for low-power single-rail logic No advantage for asynchronous dual-rail adder

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**Signed-Magnitude Arithmetic**

Addition/Subtraction Need absolute value generation 0, , ( = ) We want to get 1, as a result But conventional 2’s complement adder don’t – = (oops!) So, calculate both A-B,B-A and choose the positive number if two operands differ the sign bit. If two operands differ the sign bit, addition becomes subtraction in signed-magnitude arithmetic. If both operands have the same sign, operation will be addition not subtraction. (Choose any of them, it’s ok.)

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**Signed-Magnitude Arithmetic**

Multiplication In 2’s complement representation, multiplication is a bit complex than you think. -1 x x 0001 1111 0000 = 15 (not -1) Therefore array multiplier will not work!

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**Signed-Magnitude Arithmetic**

Multiplication (continued) Solution for 2’s complement Sign extension for each partial product: will make multiplier array much bigger Use Baugh-Wooley multiplier: can reduce sign-extension bits significantly – very popular algorithm for DSP hardware Good news for signed-magnitude is that integer and fraction parts are unsigned. Can use unsigned multiplier (array multiplier) Sign bit manipulation is easy (+) x (-) = (-), etc.

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**Fixed-Point Arithmetic**

Range of coefficient =< h[k] <= One bit is integer and seven bits are fraction (MSB is sign bit) Range of input and output b < x/y[n] < b Need 8x8 multiplier (3 integer, 13 fraction field) Need 16-bit accumulator (3 integer, 13 fraction) Keep full 16-bit precision for intermediate results Watch out overflow! We will use saturated arithmetic. Saturated arithmetic: overflow sets output to maximum value (1, or 0, ) For final value, rounding is required (make even if tie)

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**Fixed-Point Arithmetic**

“Make even if tie” rounding Out of 16-bit intermediate result, we pick two integer field and 6 fraction field with sign bit. S I I I . F F F F F F X X X X X X X If XXXXXXX = , round up or truncate have the same round-off error. In this case, round up only if the final result after rounding becomes even number. =>round up=> (even) =>truncate=> (even) If XXXXXXX = 0xxxxxx, truncate If XXXXXXX = 1xxxxxx (at least one of x’s is 1), round up

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**Front-End Deliverables**

2 weeks (Nov 14th) Async Teams (1-4 people) Cell-based design Structural down to cells Behavioral verilog for each cell Simple estimated delay model Slack matching not necessary Testbench complete Initial correct streams (given to you next week) Final input streams to be given in last week

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**Front-End Deliverables**

2 weeks (Nov 14th) Sync Teams (1-4 people) RTL design Separate FSM Verilog and Datapath components Routing logic (mux), pipeline registers, counters explicit FSM design Separate next-state, output logic, and state memory verilog processes Explicit control signals Behavioral verilog for each datapath component Simple estimated delay model Testbench complete Initial correct streams (given to you next week) Final input streams to be given in last week

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**Front-End Deliverables**

2 weeks (Nov 14th) Common Deliverables Cadence schematics Verilog code for all blocks Verilog sims Report Architectural description and all assumptions Performance estimation Breakdown of work within team members

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**Back-End Deliverables**

Common Deliverables (Dec 10th) Critical path analysis Justification of critical path / cycle Wire load estimation Transistor sizing of critical path (or critical cycle) Hspice of critical path Magic layout of critical path (or critical cycle) Size estimation HSPICE of extracted layout (no resistance extracted) Floorplan Size estimation of key blocks and of entire core Report Architecture update / revisions Description of back-end efforts

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**Presentation 5 minutes + 5 * # of team members**

Overview of front and back-end designs Same time as would be final exam Grading 30% Front-End Report 40% Final Report 30% Presentation

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