Mode-Controlled Dataflow Modeling of Real-Time Memory Controllers

Transcription

1 Mode-Controlled Dataflow Modeling of eal-time Memory Controllers Yonghui Li, Hrishikesh alunkhe, Joao Bastos, Orlando Moreira 2, Benny Akesson 3 and Kees Goossens Eindhoven University of Technology, the Netherlands 2 Intel corporation, 3 CITE/INEC TEC, IEP, Portugal yonghui.li@tue.nl

2 Introduction: DTV & TB NXP Viper2 (PNX8550) 0.3 m orst-case Bandwidth (CB)? MIP P4450 M Memory Controller TM32 M M TM32 ~50 M transistors ~00 clock domains more than 70 IP blocks MPEG MIP MDC VIP MP MB MCU TDC QVCP5L TriMedia # TriMedia #2 QVCP2L DC-EC DC-CT M-GIC M-IPC CLOCK GLOBAL EET TM-DBG TM2-DBG UAT UAT2 UAT3 EJTAG BOOT M M M M-DC PMA-MON PMA-EC PMA-AB M PCI/XIO DE IIC IIC2 IIC3 UB MC MC2 M-Gate C-Bridge PMA DC-EC DC-CT VMPG DVDD EDMA VLD QVCP2 MB MB2 QTN QVCP VIP VIP2 VPK TDMA T-DC TM-IPC TM-GIC M M M M M M TM2-IPC TM2-GIC DENC PDIO AIO AIO2 AIO3 GPIO TUNNEL MP MP2

3 Outline Background DAM Dynamically scheduled memory controller Dataflow modeling of command scheduling hy dataflow modeling? Mode-controlled dataflow (MCDF) MCDF modeling of a memory controller Experimental results Conclusions 2

4 DAM Memories DAM is accessed by scheduling commands ACT, PE, D,, EF, NOP ubject to timing constraints cmd addr. data DAM Activate (ACT) ead (D) Bank Bank 7 Bank 0 ow buffer ow buffer ow buffer rite () Precharge (PE) ACT NoP D NoP ACT NoP NoP PE NoP PE NoP 3

5 Dynamically cheduled Memory Controller A transaction is translated into a sequence of commands Trans Command Generator Log. Addr. Data Memory Map Cmd queue Phy. Addr. cmd Bank 7 Bank Bank 0 ow buffer ow buffer ow buffer Timing Counters cheduler DAM cheduling algorithm First-Come First-erve (FCF) for transactions D or commands have higher priority than ACT 4

6 cheduling Dependencies of a Transaction A transaction T i is executed by scheduling commands to successive banks twitch td tp ta tcd tccd ttp ACT PE tp Bank 0 T i tfa tp tfa tp tfa td tccd ta tcd tccd ttp tp ACT PE Bank td td tccd tccd Bank BI i ta tcd tccd ttp tp ACT PE twitch orst-case analyses have been carried out based on analyzing individual dependency [4][0][][2] 5

7 Dataflow Modeling of Command cheduling From command scheduling to actor firing Commands are represented by actors Timing constraints are captured by delay actors cheduling dependencies are depicted by the edges between actors D D CCD tccd ACT tcd D ACT CD D t D j = max{t ACT j + tcd, t D j + tccd} Max-plus algebra 6

8 Dataflow Modeling of Command cheduling Dataflow model of transactions (e.g., 32-byte read) Command scheduling dependencies ACT D PE tccd ttp td tccd ACT tcd D A ta ttp ta PE tp tp ACT CD D TP PE Bank 0 Bank P Dynamisms Unknown order of accessing different banks Variable transaction sizes need different number of banks D CCD CCD ACT CD D TP PE P A ingle rate dataflow graph (DF) 7

9 Outline Background DAM Dynamically scheduled memory controller Dataflow modeling of command scheduling hy dataflow modeling? Mode-controlled dataflow (MCDF) MCDF modeling of a memory controller Experimental results Conclusions 8

10 Mode-Controlled Dataflow Model (MCDF) MCDF is a restricted variant of Boolen data-flow capturing dynamisms while being analyzable The structure includes actors behaving as switch and select, which are controlled by an actor, named mode-controller (MC) 0 M0 A, 0 0 rc, 2 switch Tunnel select M = M 0 M M 0 M 0 M M B, 2 MC Dynamism is captured by defining mode sequences (M) 9

11 Mode-Controlled Dataflow Model (MCDF) 0 M0 A, 0 0 MCDF & M rc, 2 switch Tunnel select MC M B, 2 M 0 = M 0 M = M ingle rate dataflow graph (DF) rc A L Tsw a Tsl rc B L Tsw b Tsl MC MC 0

12 Mode-Controlled Dataflow Model (MCDF) 0 M0 A, 0 0 MCDF & M rc, 2 switch Tunnel select MC M B, 2 M = M 0 M ingle rate dataflow graph (DF) rc A_ L_ Tsw_ a_ Tsl_ rc_2 _2 B_ L_2 Tsw_2 b_ Tsl_2 MC_ MC_2

13 Mode-Controlled Dataflow Model (MCDF) M = M 0 M MCDF & M Tsl_ L_ L_2 Tsl_2 a_ A_ B_ b_ Tsw _2 Tsw_ 2 ingle rate dataflow graph (DF) MC_ rc_ rc_2 MC_2 2

14 Mode-Controlled Dataflow Model (MCDF) M = M 0 M MCDF & M Tsl_ L_ L_2 Tsl_2 a_ A_ B_ b_ M 0_0 M _0 Tsw _2 Tsw_ 2 ingle rate dataflow graph (DF) MC_ rc_ rc_2 MC_2 3

15 Mode-Controlled Dataflow Model (MCDF) M = M 0 M MCDF & M M 0_0 M _0 M 0_ M _ ingle rate dataflow graph (DF) M 0_2 M _2 4

16 Outline Background DAM Dynamically scheduled memory controller Dataflow modeling of command scheduling hy dataflow modeling? Mode-controlled dataflow (MCDF) MCDF modeling of a memory controller Experimental results Conclusions 5

17 MCDF Modeling of Dynamic Command cheduling Mode_0 ACT, 2 Bank 0 Mode_ Mode_7 ACT, 2 ACT, 2 Bank Bank 7 TC: ACT TC: ACT PE Mode_8 PE, Bank 0 Mode_9 Mode_5 PE, PE, Bank Bank 7 TC: PE Mode_6 D, Mode_7, 6

18 Mode tunnel: D Mode tunnel: FA D D D FA FA FA ACT, 2 Mode_0 ource, Mode switch Mode_8 Mode_9 CD ACT, 2 CD Mode tunnel: CD ACT, 2 CD A Mode tunnel: A PE, A PE, A Mode_ Mode_7 P P Mode tunnel: P Mode tunnel: P Mode select Mode_5 PE, P Mode tunnel: P CCD Mode tunnel: CCD Mode tunnel: TP D, TP Mode_6 T Mode tunnel: T Mode tunnel: T T Mode controller Mode_7, TP Mode tunnel: CCD CCD 7

19 MCDF Modeling of Dynamic Command cheduling From transactions to mode sequences Transaction Commands Mode sequence 32-byte read: Bank0, Bank Bank2, Bank3 Bank4, Bank5 Bank6, Bank7 ACT D PE tccd ttp td tccd ACT tcd orst-case bandwidth (CB) D ttp PE Bank 0 Bank From Maximum cycle mean (MCM) to CB C MCM = max C G ω(c M 0 = M 0 M 6 M 8 M M 6 M 9 M = M 2 M 6 M 0 M 3 M 6 M M 2 = M 4 M 6 M 2 M 5 M 6 M 3 M 3 = M 6 M 6 M 4 M 7 M 6 M 5 M = M 0 M M 2 M 3 ω(c (C CB = min C G C f mem e ref 8

20 MCDF Modeling of Dynamic Command cheduling MCDF model Commands are captured by cmd actors Timing constraints are described by delay actors A mode is constructed with these actors Transactions are translated in to static mode sequences (M) Cmds ACT... Timing constraints tcd... Mode_0 M 0 Actors ACT, Actors tcd Mode_ Mode_8 M M N T 0 T T N Trans Trace MCDF Graph Cmd cheduling Memory traffic 9

21 Experiments Goal Validate the MCDF model Obtain the worst-case bandwidth results etup Heracles: a temporal analysis tool developed at Ericsson TMemController: an open-source analysis tool for a real-time memory controller with dynamic command scheduling. 6-bit DD3-800D/600G/233K DAMs with 2Gb Transaction sizes include 6-byte, 32-byte, 64-byte, 28-bytes, 256-byte 20

22 Experiment Validation of the MCDF model imulating the MCDF model with Heracles gives identical command schedules as the cycle-accurate TMemController simulator 2

23 CB (MB/s) Experiment 2: Fixed transaction sizes MCDF >= Analytical MCDF < cheduled only for 6-byte due to cmd collision MCDF dynamic(scheduled [4]) dynamic(analytical [4]) Transaction sizes (bytes) [4] Y. Li et.al. Architecture and analysis of a dynamically-scheduled real-time memory controller. eal-time ystems Journal, pp -55, pringer,

24 CB (MB/s) Experiment 3: Variable transaction sizes 64-byte transactions followed by 28-byte transactions (known order) andom mix of 64-byte and 28-byte transactions (unknown order) transaction order(unknown) MCDF scheduled [4] analytical [4] 23 transaction order(known) [4] Y. Li et.al. Architecture and analysis of a dynamically-scheduled real-time memory controller. eal-time ystems Journal, pp -55, pringer, 205.

25 Conclusions A mode-controlled dataflow (MCDF) model of dynamic command scheduling for T memory controllers upports simulation Provides worst-case bandwidth results Is easy to adapt to other memory controllers The MCDF model outperforms several existing analysis approaches 24

26 Thank You. 25