程序代写代做代考 algorithm BPC

BPC

1

Hybrid TAGE & Perceptron
Branch Predictor

Zhenyu Wu

TAGE Predictor

2

Prediction Computation
n Base  predictor  𝑇”

q PC-­indexed  3-­bit  saturating  counter
q Giving  default  prediction

n Tagged  predictor  𝑇#(1 ≤ 𝑖 ≤ 4)
q 𝑇# are  indexed  using  a  geometric  series  of  history  length  {𝐿 𝑖 =
(𝑖𝑛𝑡)(𝛼#01 ∗ 𝐿 1 + 0.5)}

q 11-­bit  tag,  2-­bit  unsigned  useful  counter  u,  3-­bit  signed  counter  pred
q Giving  prediction  on  a  tag  match
q Provider  component  &  altpred

3

Updating Policy

n Update  the  useful  counter  u
q u is  updated  when  altpred is  different  from  final  pred
q Increment  if  pred is  correct,  decrement  otherwise
q Reset  in  period  of  256K  branches

n Update  the  pred counter  of  the  provider  component  on  a  
correct  prediction

n The  overall  prediction  is  incorrect
q Update  the  pred counter  of  the  provider  component  𝑇#
q If  𝑖 < 𝑀,  allocate  an  entry  on  a  predictor  component  𝑇:(𝑖 < 𝑘 < 𝑀) q Read  𝑀 − 𝑖 − 1 𝑢> from  𝑇>(𝑖 < 𝑗 < 𝑀) 4 Updating Policy (Cont.) n Rules  for  new  entry  allocation q Priority  for  allocation n If  exits  k,  such  that  𝑢: = 0,  then  𝑇: is  allocated n Else  the  u  counters  from  the  components  𝑇> (𝑖 < 𝑗 < 𝑀) are  all  decremented q Avoiding  ping-­phenomenon n If 𝑇> &  𝑇: can  be  allocated,  then  𝑇> is  chosen  with  higher  probability.

q Initializing   the  allocated  entry  
n pred counter  set  to  weak  correct
n u useful  counter  set  to  strongly  not  useful

5

Perceptron Predictor

6

Prediction Computation

n A  perceptron  is  represented  by  a  vector  of  signed  integer  
weights  (𝑤”..A)
q 𝑤” serves  as  bias

n The  input  is  the  global  history  record  (𝑥1..A)
q 𝑥” is  always  set  to  1,  providing  a  bias  input
q 𝑥# is  either  -­1  (NT)  or  +1  (T)

n The  output  y  of  the  perceptron  is  computed  as  
q 𝑦 = 𝑤” + ∑ 𝑥#𝑤#A#E1
q Predict  to  take  if  𝑦 ≥ 0,  not  to  take  if  𝑦 < 0 7 Updating Policy n Using  the  following  algorithm  to  train  the  perceptron q 𝜃 is  the  threshold  parameter  to  decide  when  enough  training  has   been  done q 𝜃 = 1.93ℎ + 14   8 Combining Branch Predictors 9 How to combine TAGE with Perceptron to make better prediction? n The  combined  predictor  contains  2  predictors:  TAGE  &   Perceptron n Using  a  2-­bit  saturating  counter  to  select  better  predictor n Each  counter  keeps  track  of  which  predictor  is  more   accurate  for  the  shared  branches 10 Storage Computation n Perceptron q 512  perceptron q 8-­bit  unsigned  integer  weight q 64  weights  (1  for  bias)  per  perceptron q 512×8×64  𝑏𝑖𝑡𝑠 = 32𝐾𝐵 n TAGE q 𝑇":21T×3  𝑏𝑖𝑡𝑠 = 3KB q 𝑇1:21W× 5+ 11  𝑏𝑖𝑡𝑠 = 8𝐾𝐵 q 𝑇W:21W× 5 + 10  𝑏𝑖𝑡𝑠 = 7.5𝐾𝐵 q 𝑇T:21W× 5 + 9  𝑏𝑖𝑡𝑠 = 7𝐾𝐵 q 𝑇T:21W× 5 + 8  𝑏𝑖𝑡𝑠 = 6.5𝐾𝐵 q 3 + 8 + 7.5 + 7 + 6.5 = 32KB n Combining 32 + 32 = 64𝐾𝐵 11