Run-Time Power-Down Strategies for Real-Time SDRAM Memory Controllers Karthik Chandrasekar 1, Benny Akesson 2, and Kees Goossens 2 1 TU Delft and 2 TU Eindhoven, The Netherlands Karthik Chandrasekar TU Delft

Save No Performance Impact Context here: SDRAM Memories 2

Problem Statement & Proposed Solutions  SDRAMs contribute significantly to SoC energy profile, even when idle.  Powering down impacts performance, due to power-up latencies.  Existing SDRAM memory controllers provide :  Either “Low power consumption” or “Real-Time performance” not “Both”.  Other existing real-time low-power solutions use compile-time info and are not suitable for run-time memory controller use.  We propose :  Run-time power optimization solutions for real-time SDRAM controllers.  We guarantee :  Significant energy savings without impacting bandwidth guarantees.  We support :  SDRAM memory controllers using Predictable arbiters such as: Round-Robin, Time Division Multiplexing, Priority-based arbiters etc. 3

Arbiters, Requests & Guarantees  Predictable Arbiters such as Round-Robin, TDM, etc. provide:  Maximum Latency Bounds  Minimum Bandwidth Guarantee  Such performance guarantees are based on :  Request Sizes & Service Cycle Length (SCL)  The smallest SCL (min_SCL) defines Scheduling Interval (SI) and Idle SCL.  The longest SCL (max_SCL) defines the guaranteed Net Bandwidth.  Micron 1Gb, DDR3-800 using Closed-Page BC-4, BI-1 for 64B requests. 4

Deriving Latency-Rate Arbiter Guarantees  A Latency-Rate arbiter guarantees a requester :  Maximum Latency Bounds  Minimum Bandwidth Guarantee  Deriving guarantees for R1 when backlogged using Round-Robin arbiter  Maximum Latency Bound( Θ ) = t BLOCK + (x+1) * max_SCL + t REFRESH  Net Bandwidth (Net_BW) = num(max_SCL) * Request Size / t REFI  Minimum Guaranteed Bandwidth (β) = ρ* Net_BW 5

Proposed Real-Time Power-Down Strategies  Conservative Power-Down  Always powers-up within Scheduling Interval (SI)  Aggressive Power-Down  Powers-up only when required; with Snooping SI – tPUP  Request misses slot, if it arrives after Snooping point  Only latency bounds increase and bandwidth guarantee is not affected.  What if the request arrives after Snooping point?

Impact on Θ and β  Conservative Power-Down  Θ does not change  Max_SCL does not change  Aggressive Power-Down  Θ increases by tPUP  Max_SCL does not change  Speculative Power-Down  Max_SCL increases  Latency Bound( Θ ) = t BLOCK + (x+1) * max_SCL + t REFRESH  Net Bandwidth (Net_BW) = num(max_SCL) * request size / t REFI  Bandwidth Guarantee (β) = ρ* Net_BW  Θ increases depending on number of interfering requesters (x)  Net_BW and β decrease significantly depending on increase in max_SCL 7

Impact on Energy & Performance 8  Worst-Case Impact:  Θ Increase:  Aggressive PD – 2.4%  Speculative PD – 12.3%  β Decrease:  Aggressive PD – 0.0%  Speculative PD – 12.1%  Average Execution Time Penalty:  Aggressive PD – 0.25%  Speculative PD – 1.32%  Energy Savings:  Conservative PD – 42.1%  Aggressive PD – 51.3%  Theoretical Best PD – 51.4%  4 Requesters/Apps, Round-Robin, Micron 1Gb, DDR3-800, 64B requests

Summary  Proposed two real-time power-down strategies:  Conservative Latency-Bandwidth-Neutral and Aggressive Bandwidth-Neutral  If memory goes idle, it powers-down (if it is gainful to power-down). Run-time, it checks if the memory can go to or continue to be in power-down.  Evaluated their impact on:  Latency Bounds (Θ)  Bandwidth Guarantee (β)  Compared them against:  Speculative power-down  Theoretical best power-down  Showed impact on:  Real-time performance guarantees  Average-case execution time and energy savings For more details: Please visit my poster! 9