VLSI Signal Processing
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1 VLSI Signal Processing Lecture 1 Pipelining & Retiming ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-1
2 Introduction DSP System Real time requirement Data driven synchronized by data Programmable processor ASIC or custom DSP DSPs What to use depends on requirements Throughput Power, Energy Area Wordlength precision Flexibility Time-to-market Time-in-market ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-2
3 Introduction Selecting Application Processors for Mobile Multimedia ( 30CDC_Application_Processors.pdf) ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-3
4 DSP Representations DSP Algorithm are non-terminating y[ n] = h0x[ n] + h1 x[ n 1] + h2x[ n 2] + h3x[ n 3] Iteration period: the time for one iteration, one execution of the loop within DSP algorithm Critical path: longest path without delay Critical path sets bound on clock frequency, i.e. T clk T critical Sampling rate: number of samples processed / second Latency: time difference between output and corresponding input (combinational logic = gate delays, sequential logic = number of clock cycles) The clock rate (or frequency) of the DSP system is not the same as its sampling rate ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-4
5 Block Diagram The graphical representations of DSP algorithm exhibit all the parallelism (concurrence) and datadriven properties (dependence) of the system, as well as provide insight into space and time tradeoffs. Consists of functional blocks connected with directed edges, which represent data flow from its input block to its output block y[ n] = h0 x[ n] + h1 x[ n 1] + h2x[ n 2] + h3x[ n 3] ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-5
6 Signal-Flow Graph (SFG) Nodes: represent computations and/or task Directed edge (j,k): denote a linear transformation from the input signal at node j to the output signal at node k Source: no entering edge Sink: only entering edges y[ n] = h0x[ n] + h1 x[ n 1] + h2x[ n 2] + h3x[ n 3] ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-6
7 Transposition of an SFG Applicable to linear SISO systems Flow graph reversal Reserve the direction of all edges Exchange input and output y[ n] = h0x[ n] + h1 x[ n 1] + h2x[ n 2] + h3x[ n 3] ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-7
8 Data-Flow Graph (DFG) DFG captures the data-driven property of the DSP algorithm Nodes represent computations, functions, or tasks Directed edges represents data flow with non-negative number of delays A node can execute whenever all its input data are available Each edge describes a precedence constraint between two nodes in DFG Intra iteration precedence constraint: if no delay Inter-iteration precedence constraint: if existing one or more delays y[ n] = h0x[ n] + h1 x[ n 1] + h2x[ n 2] + h3x[ n 3] ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-8
9 Examples of DFGs DFGs and Block diagrams can be used to describe both linear single-rate and nonlinear multi-rate DSP systems. Synchronous DFG (SDFG): the number of input and output samples are specified at each node in each execution. Multi-rate SDFG 3f A =5f B Decimator (down-sampling) Expander (up-sampling) 2f B =3f C ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-9
10 Critical Path fundamental limit 4 possible paths Input nodes to delay element Input node to output node Delay element to delay element Delay element to output Critical path: the path with the longest computation time among all paths without delay elements Clock period is bounded by the computation time of the critical path Example: 5-tap FIR filter and assume T A =4ns, T M =10ns critical paths = 26ns ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-10
11 Remarks 10-bit Direct Form 4-tap FIR 10-bit Transposed Form 20-bit 20-bit 1. T Critical =T M +(N-1) T A, where N is the number of taps 2. Short word-length delay elements 1. T Critical =T M +T A 2. High fan out causes high driving force 3. Long word-length delay elements ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-11
12 Iteration Period Iteration: execution of all computations of an algorithm once Iteration period: the time required for execution of on iteration T A + T M A 0 B 0 A 1 B 1 A 2. Iteration rate: the number iterations executed per second Sample rate (throughput): the number of samples processed per second ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-12
13 Loop Bound Loop: a directed path that begins and ends at the same node Loop bound of the loop j: T j / W j, where T j is the loop computation time and W j is the number of delays in the loop Examples: assume T A +T M =6ns, a, b, c, a is a loop with loop bound = 6ns T T + T assume T A +T M =6ns, the loop bound = 3ns s A M y(n)=ay(n-2)+x(n) ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-13 T s T A + T M 2
14 Remarks retiming A N B N+1 A N+2 B N+3 ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-14
15 Iteration Bound fundamental limit Critical loop: the loop with the maximum loop bound Iteration bound: the loop bound of the critical loop Not possible to achieve iteration period lower than iteration bound even with infinite processing power Definition Example ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-15
16 Algorithms for Computing Iteration Bound Longest Path Matrix (LPM) Algorithm Minimum Cycle Mean (MCM) Algorithm ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-16
17 LPM Algorithm (1/2) d: number of delays, d i : ith delay element Construct matrices L (m), 1 m d, where each entry l (m) i,j is the longest path from d i to d j with m-1 delays l (m) i,j =-1 if no such path L (m+1) can be obtained from L (1 ) and L (m ) recursively by, if there is k such that l (m+1) i,j = l (1) i,k + l (m) k,j, otherwise l (m) i,j =-1 Note that the diagonal element represents the longest computation time of all loops with m delays contains d i Then the iteration bound can be calculated by T =max{l (m) i,i / m}, for 1 i,m d ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-17
18 LPM Algorithm (2/2) ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-18
19 T of Multirate DFGs (MRDFGs) Construct the equivalent single-rate DFG (SRDFG) Compute the iteration bound of the equivalent SRDFG (unrolling or unfolding) The iteration bound of the MRDFG is the same as the iteration bound of the equivalent SRDFG. 3f A =5f B 2f B =3f C f B =(3/5)f A f c =(2/3)f B =(2/5)f A ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-19
20 Transformation of MRDFG k x : the number of nodes related to x Note: same number of delay elements ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-20
21 Pipelining & Parallel Processing Pipelining processing By using pipelining latches to reduce critical path Either to increase the clock speed or sample speed, or to reduce the power consumption at same speed Parallel processing By using replicating hardware to process multiple samples in parallel in a clock period The effective sampling speed is increased by the level of parallelism. Can be used to the reduction of power consumption continue sending T sample =T CLK Tsample T CLK ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-21
22 Parallel Processing Parallel processing and pipelining processing are duals each other Parallel processing is just the block processing. The number of inputs processed in a clock cycle is referred to as the block size. Block delay delay y( n) = ax( n) + bx( n 1) + cx( n 2) y(3k) y(3k + 1) y(3k + 2) = = = ax(3k) + bx(3k 1) + cx(3k 2) ax(3k + 1) + bx(3k) + cx(3k 1) ax(3k + 2) + bx(3k + 1) + cx(3k) ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-22
23 Pipelining Latches Direct Form 4-tap FIR 2-level pipelined architecture Feed-forward pipelining latches 1. T Critical =T M +(N-1) T A, where N is the number of taps 2. Clock speed limited by critical path 1. T Critical =T M +T A 2. Pipelining will not affect functionality but introduce latency ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-23
24 Cutset Pipelining (1/2) Cutset: a set of edges of a graph such that if these edges are removed from the graph, the graph becomes disjoint Feed-forward cutset: the data move in the forward direction on all edges of the cutset In a synchronous DFG (SDFG), communications occurs on the edges only data flow through the functional units directly and the operations are synchronized with the arranged data arrival times (via delay insertion) ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-24
25 Cutset Pipelining (2/2) Cutset pipelining preserves architecture s behavior the data movement between the two disjoint sub-graphs only occurs on the feed-forward cutset, delaying or advancing the data movement along all edges on the cutset by the same amount of time do not change the architecture s behavior Two main drwaback cutset Increase in the number of latches + kd System latency G1 +kd + kd G2 ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-25
26 Feed-forward Cutset D D D Must place delays on all edges in the cutset Not a valid pipelining ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-26
27 Retiming Retiming is to move around existing delays s.t. Does not alter the latency of the system Changes (Reduces) the critical path of the system Reduces the number of register Pipelining is equivalent to introducing many delays at the input followed by retiming ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-27
28 Example -- Cutset Retiming cutset Example + kd cutset G1 + kd - kd G2 A B 2D D D D D F Add delays to edges going one way and remove from ones going the other C D D E ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-28
29 Example Node Retiming To cutset around one specified node ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-29
30 Node Retiming The cutset can be selected such that one of the disjoint subgraphs contains only one node Retiming value r(v): defined as the number of delays moved from each output edge of the node V to each of its input edges Feasibility constraints: for each directed edge UV of the retimed SDFG, the number of delay elements must be non-negative where w r () e and w() e after and before retiming respectively For any SDFG, there is a retiming design space defined by the system of inequalities (each edge contributes an inequality) Solving systems of inequalities r r ( e) = w( e) + r( V ) r( U ) ( U ) r( V ) w( e) w 0 are the number of delay elements on the edgeuv ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-30
31 Node Retiming with Formation Result ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-31
32 Properties of Retiming P.1:The weight of a retimed path p = V 0, V 1,, V k is given by w r (p) = w(p)+r(v k )-r(v 0 ) P.2: Retiming does not change the number of delays in a cycle (special case of P.1 with V k =V 0 ) P.3: Retiming does not alter the iteration bound in a DFG as the number of delays in a cycle does not change P.4: Adding a constant value j to the retiming value of each node does not change the mapping from G to G r ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-32
33 Pipelining Feedforward Cutset A special case of cutset retiming by placing delays at feedforward cutset ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-33
34 Remarks 3-stage Lattice Filter T Critical =2T M +(N-1) T A, where N is the number of taps Cutset retiming T Critical =4T M +4T A, a constant, which is independent of N ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-34
35 K Slow-down and Retiming 2-slow transformation Replace each D by KD 1. K-1 null operation must be inserted 2. Hardware utilization is reduced (e.g. 50%) Retiming 2-slow version D T clk =1 t.u., T iter =2 t.u. Hardware can be fully utilized if two independent operations are available ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-35
36 Cutset Retiming 2-slow Lattice Filter T Critical =2T M +(N-1) T A, where N is tap number x 0 0 x 1 0 x 2 0 x 3 0 Inserting of 0 to preserve behavior!! T sample = 2 T critical 2-slow ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-36
37 Retiming for Min Clock Period (1/3) ( ) Φ( G) = max { t( p) : w( p) = 0} Φ G : the minimum feasible clock period is defined as In the retiming design space (defined by the system of inequalities), find a retimed SDFG with the minimum Φ( G) Definition: W(U,V): the minimum number of registers on any path from node U to node V (, V ) min w( p) W U p { U V } = : D(U,V): the maximum computation time among all paths from U to V with weight W(U,V) { } p (, V ) = max t( p) : U V and w( p) = W ( U, V ) D U w(p) denotes the delays of the path t(p) denotes the computation time of the path ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-37
38 Retiming for Min Clock Period (2/3) Step 1: Compute W(U,V) and D(U,V) Let M = t max n, where t max is the maximum computation time of the nodes in G and n is the number of nodes in G Form a new graph G, which is the same as G except the edge weights are replaced by w (e) = Mw(e) t(u) for all edges from U to V Solve the all-pair shortest path problem on G (using Floyd- Warshall algorithm). Let S UV be the shortest path from U to V If U = V, W(U,V) = 0 and D(U,V) = t(u) else, W(U,V) = ceiling (S UV /M) and D(U,V) = MW(U,V) - S UV + t(v) ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-38
39 Retiming for Min Clock Period (3/3) Step 2: Given a clock period c, find a feasible solution such that Φ( G) c The retimed design space is now defined by feasibility constraints r U r V w e ( ) ( ) ( ) r(u)-r(v) W(U,V) critical path constraints i.e. r ( U ) r( V ) W ( U, V ) 1 for all nodes U and V in G, such that if (, V ) > c D U (, V ) > c, r( U ) r( V ) W ( U, V ) 1 W ( U, V ) + r( V ) r( U ) < 1, D( U, V ) c D U if If the system of inequalities is not φ, try a smaller c ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-39
40 Retiming for Register Minimization Find a feasible solution with minimum number of registers in the design space constrained by feasibility and critical-path constraints (the refined retimed space with minimum clock period) Multiple fan-out problem D V 1 V 1 U 3D V 2 V 2 7D V 3 U D 2D 4D V 3 Modeled as an integer linear programming (ILP) problem Minimize q( U ), while satisfying longest fanout constraints w( e) + r( V ) r( U ) q( U ) feasibility constraints r( U ) r( V ) w( e) critical path constraints r U r V W U, V 1 ( ) ( ) ( ) for all nodes U and V in G, such that (, V ) > c D U Note solving the ILP model is NP-hard, and a gadget model can be found in the literature. ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-40
41 Pipelining & Parallel Processing Pipelining processing By using pipelining latches to reduce critical path Either to increase the clock speed or sample speed, or to reduce the power consumption at same speed Parallel processing By using replicating hardware to process multiple samples in parallel in a clock period The effective sampling speed is increased by the level of parallelism. Can be used to the reduction of power consumption continue sending T sample =T CLK Tsample T CLK ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-41
42 Pipelining & Parallel Processing (2/1) Communication bounded When the critical path is less than that the I/O bound, i.e. output-pad delay plus input-pad delay and the wire delay between two chips, then the system is communication bounded. Pipelining can be used only to the extent such that the critical path is limited by the communication bound. Once this is reached, pipelining can no longer increase the speed ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-42
43 Pipelining & Parallel Processing (2/2) M-level pipelining processing The critical path is reduced to 1/M L-level parallel processing The critical path of the block processing system remains unchanged. L samples are processed in 1 (not L) clock cycle, the iteration period is T iteration =T sample =T clk /L, where T clk T critical By combining M-level pipelining and L-level parallel processing, then T iteration =T sample =(1/LM)T clk ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-43
44 Power Consumption in CMOS P total =P dynamic +P short-circuit +P static Short circuit Power Consumption: current spikes Static Power Consumption: due to leakage current ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-44
45 CMOS Power Consumption (Dynamic) power dissipation 2 P = Ctotal V 0 f C total : the total capacitance of the CMOS circuit Propagation delay Ccharge V0 Tpd = k V ( ) 2 0 C charge : the capacitance to be charged or discharged in a single clock cycle (along the critical path) V 0 : the supply voltage; V t : the threshold voltage K : technology parameter V t ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-45
46 Reduce Capacitances Transistor/Gate C Load C Interconnects External Activity Frequency Power supply Some comments Off-chip connections have high capacitive load System integration ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-46
47 Pipelining for Low Power (1/2) For an M-level pipelined architecture, the critical path is reduced to 1/M and the capacitance to be charged or discharged in a single cycle (i.e. C charge ) is also reduced to 1/M If the same clock speed is maintained (i.e. f = 1/T pd ), only 1/M of the non-pipelined capacitance is required to be charged or discharged, which suggests voltage reduction Suppose the voltage can be reduced to β V 0, the power consumption of a pipelined architecture becomes P pipelined = C 2 = β total P ( β V ) 0 2 non pipelined f ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-47
48 Remarks Propagation delay of the original filter and the pipelined filter ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-48
49 Pipelining for Low Power (2/2) Solving propagation delay of the original architecture Ccharge V0 Tnon-pipelined = k V ( ) 2 propagation delay of the pipelined architecture Ccharge β V0 T M pipelined = k β V setting the above two equations equal, the following quadratic equation can be obtained to solve β M 0 V t ( ) 2 0 V t 2 ( β V V ) = β ( V V ) 2 0 t 0 t ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-49
50 Example: Reduce Power by Pipelining Consider the following two FIR filters. x(n) x(n) m1 m1 m1 D D D D D y(n) m2 m2 m2 D D y(n) Assume the multiplication and the addition take 10 u.t. and 2 u.t. respectively the capacitance of the multiplier is 5 times that of an adder In the fine-grain pipelined filter, the multiplier is broken into m1and m2, with 6 and 4 u.t. computation delay and 3 and 2 times capacitance of an adder supply voltage V 0 and threshold voltage V t are 5 and 0.6 respectively Problems: What is the supply voltage of the pipelined architecture if the clock periods are identical? What is the relative power consumption? ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-50
51 Solution Calculate C charge : let C M, C A, C m1 and C m2 be the capacitance of the multiplier, the adder, and the two fine-grain pipelining parts of the pipelined FIR filter respectively Non-pipelined: C charge = C M + C A = 6C A Pipelined: C charge = C m1 = C m2 +C A = 3C A Notice the pipelining level M = 2 and the charging capacitance of the pipelined filter is one-half of that of the original one Solve 2 2 β = β β β ( ) ( ) β = 0 = or (invalid, Qβ = < The supply voltage of the pipelined filter is V pipelined = β V = V 0 V t ) Power consumption ratio is β 2 = 36.4% ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-51
52 Parallel Processing for Low Power (1/2) For an L-parallel architecture, the charge capacitance remains the same, but the total capacitance (i.e. C total ) is increased L times The clock speed of the L-parallel architecture is reduced to 1/L (i.e. f = 1/LT pd ) to maintain the same sample rate, which means the C charge is charged or discharged L times longer. The supply voltage can be reduced to β V 0, since more time is allowed to charged or discharged the same capacitance. Thus the power consumption of the parallel architecture becomes P parallel = ( L C ) ( β V ) 2 = β P total non parallel 0 2 f L ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-52
53 Remarks Propagation delay of the original filter and the parallel filter ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-53
54 Parallel Processing for Low Power (2/2) Solving β propagation delay of the original architecture T non-parallel charge 0 V t ( V ) 2 propagation delay of the parallel architecture T parallel = C k ( β V V ) setting these two propagation delays equal, the following quadratic equation can be obtained to solve β L V Ccharge β V0 = = T 2 non-parallel k 0 t 2 ( β V V ) = β ( V V ) 2 0 t 0 t 0 L ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-54
55 Example: Reduce Power by Parallel Consider the following two FIR filters, with critical paths denoted in dash lines respectively x(2k) x(n) D y(2k+1) D D D y(n) x(2k+1) Assume the multiplication and the addition take 8 u.t. and 1 u.t. respectively the capacitance of the multiplier is 8 times that of an adder both architectures operate at the sample period of 9 u.t. supply voltage V 0 and threshold voltage V t are 3.3 and 0.45 respectively What is the supply voltage of the parallel architecture? What is the relative power consumption? D D y(2k) ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-55
56 Solution Calculate C charge : let C M and C A be the capacitance of the multiplier and the adder respectively Non-parallel: C charge = C M + C A = 9C A Parallel: C M +2C A = 10C A Notice the charging capacitance of the two architectures are not equal, and we cannot use the equation directly Solveβ 9C A V0 10C A β V0 Tnon parallel = and T 2 parallel = 2 k( V V ) k( β V V ) QT 5 β parallel β = 2 T 2 ( V V ) = 9 ( β V V ) 0 2 t 0 non parallel β t = 0 β = or (invalid, Qβ t V 0 0 = < V ) t t The supply voltage of the parallel filter is Power consumption ratio is β 2 = 43.41% V pipelined = β V = ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-56
57 Parallel Processing Block processing the number of inputs processed in a clock cycle is referred to as the block size x(n) SISO y(n) x(n) Serial to Parallel Converter x(3k) x(3k+1) x(3k+2) MIMO y(3k) y(3k+1) y(3k+2) Parallel to Serial Converter y(n) at the k-th clock cycle, three inputs x(3k), x(3k+1), and x(3k+2) are processed simultaneously to generate y(3k), y(3k+1), and y(3k+2) ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-57
58 I/O Conversion Serial to parallel converter sampling period T/3 T/3 x(n) D D x(3k+2) Parallel to serial converter x(3k+1) x(3k) y(3k+2) y(3k+1) y(3k) D T/3 D T/3 3k y(n) sampling period ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-58
59 Mathematical Formulation e.g. y(n) = ay(n-9) + x(n) 2-parallel Y(2k) = ay(2k-9) + x(2k) Y(2k+1) = ay(2k-8) + x (2k+1) In 2-parallel SDFG, one active clock edge leads two samples Y(2k) = ay(2(k-5)+1) + x(2k) Y(2k+1) = ay(2(k-4)+0) + x(2k+1) Dependency with less than # parallelism of sample delays can be implemented with internal routing ADSP Lecture1 - Pipelining & Retiming (cwliu@twins.ee.nctu.edu.tw) 1-59
Pipelining and Parallel Processing
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