1. On-demand solution to minimize I-cache leakage energy
Group members: Chenyu Lu and Tzyy-Juin Kao

2. Motivation
High power dissipation causes thermal problems, such as higher packaging, power delivery and cooling costs
In 70nm technology, leakage may constitute as much as 50% of total energy dissipation
Use the super-drowsy leakage saving technique
Lower the supply voltage to a level (0.25V) near the threshold voltage (0.2V)
Data can still maintain but can not be accessed
Require one cycle penalty to wake up from the saving mode to the active mode
Use the on-demand wakeup policy on the I-cache
Only the cache lines currently in use need to be awake
Accurately predict the next cache line by using the branch predictor
On most branch mispredictions, the extra wakeup stage is overlapped with the misprediction recovery

3. Overview Super-drowsy cache line Wakeup prediction policy
A Schmitt trigger inverter controls the voltage of the cache line at the leakage saving mode
Replace multiple supply voltage sources
Wakeup prediction policy
enables on-demand wakeup
The branch predictor already identifies which line need to be woken up
No additional wakeup-prediction structure is needed

4. Methodology
Leakage energy = drowsy_energy + active_energy + turn_on_energy
Monitoring every cycle in sim-outorder: active_lines & turn_on
Add a wake_bit to every block:
0: means it’s in drowsy mode this cycle
1: means it’s in active mode this cycle
2: means it’s in active mode this cycle and the next cycle
3: means it in drowsy mode this cycle and will be in active mode next cycle
Update the wake_bit and count the active_lines every cycle using Update_wakeup()
Change the wake_bit every instruction fetch using fetch_line()
Improved strategy Interval * Active_Power < Interval * Drowsy_Power + Turn_On_Energy
Speculate with a list of recently-accessed cache lines

5. Results

6. Change block size

7. Change interval

8. Future Work
One cycle extra latency when target address misprediction (0.08% performance drop according to the paper)
Apply On demand policy on data cache
No prediction
Extra latency can be hidden by locality and out of order execution