1. Fast Path-Based Neural Branch Prediction Daniel A. Jimenez Presented by: Ioana Burcea

2. Outline Research motivation Neuran prediction: perceptron prediction Staggered algorithm Experiments and results

3. Research motivation Branch prediction –Accuracy –Latency Neural learning predictors –Most accurate –High latency => can we do any better?

4. Branch Prediction with Perceptrons Global history shift register that stores outcomes of branches –History length h Perceptron predictor –Weight matrix: n x (h + 1) weights Every row stores a vector of h + 1 weights W 0 is often called the bias weight

5. Prediction

6. Update

7. Staggered Algorithm

8. Prediction

9. Update

10. Experiments 17 integer benchmarks (SPEC2000 & SPEC95)

11. Simulated Predictors 2Bc-gskew with two level overriding –Hybrid predictor One bimodal and 2 gshare predictors Perceptron predictor Gshare.fast Fixed length path predictor Path-based neural predictor

12. Tuned history lenghts

13. Estimated latencies

14. Average misprediction rates

15. Average IPC

16. Misprediction rates at 8KB hw