1. Multiperspective Perceptron Predictor with TAGE*
07/16/96
Multiperspective Perceptron Predictor with TAGE
Daniel A. Jiménez
Department of Computer Science & Engineering
Texas A&M University *

2. Multiperspective Idea
Instead of just global/local history, use a wide range of features
Multiple perspectives on branch history
Multiperspective Perceptron Predictor
Hashed Perceptron
Sum weights indexed by hashes of features
Update weights using perceptron training
Contribution is a wide range of features

3. Main Idea
“Statistical corrector” in TAGE-SC-L is a perceptron predictor
Augment it with multiperspective features

4. Traditional Features SC already contains some traditional features
GHIST – global branch outcome history
LOCAL – 11-bit local history
Callstack history

5. Novel Features
IMLI – from Seznec’s innermost loop iteration counter work:
When a backward branch is taken, count up
When a backward branch is not taken, reset counter
I propose an alternate IMLI
When a forward branch is not taken, count up
When a forward branch is taken, reset counter
This represents loops where the decision to continue is at the top
Typical in code compiled for size or by JIT compilers
Forward IMLI works better than backward IMLI on these traces
I use both forward and backward in the predictor

6. Novel Features cont. MODHIST – modulo history
Branch histories become misaligned when some branches are skipped
MODHIST records only branches whose PC is congruent to 0 modulo some n.
Hopefully branches responsible for misalignment will not be recorded
Try many values of n to come up with a good MODHIST feature

7. Novel Features cont. MODPATH – same idea with path of branch PCs
RECENCY
Keep a recency stack of n branch PCs managed with LRU replacement
Hash the stack to get the feature
RECENCYPOS
Position (0..n-1) of current branch in recency stack, or n if no match
Works surprisingly well

8. Novel Features cont. BLURRYPATH
Shift higher-order bits of branch PC into an array
Only record the bits if they don’t match the current bits
Parameters are the depth of the array and the number of bits to truncate
Give a view of the region a branch came from rather than the precise location

9. Novel Features cont. ACYCLIC
The current PC indexes a small array, recording the branch outcome there
The array always has the latest outcome for a given bin of branches.
Acyclic – loop or repetition behavior is not recorded

10. Putting it Together
Statistical corrector of TAGE-SC-L is augmented with these features
Each feature is computed, hashed, and XORed with the current PC
Resulting index selects weight from a table
Weights are summed, thresholded to make prediction
Weights are updated with perceptron learning

11. Optimizations
Searched for good TAGE table sizes to enable sufficient perceptron table sizes
Optimized TAGE hash functions for ARM
Adaptive threshold training
Hashing some tables together with IMLI and RECENCYPOS

12. Contribution of Features (64KB)

13. Results
8KB – MPKI
64KB – MPKI
Unlimited – MPKI