RRAM-VAC: A Variability-Aware Controller for RRAM-based Memory Architectures

The growing need for connected, smart and energy efficient devices requires them to provide both ultra-low standby power and relatively high computing capabilities when awoken. In this context, emerging resistive memory technologies (RRAM) appear as a promising solution as they enable cheap fine grain technology co-integration with CMOS, fast switching and non-volatile storage. However, RRAM technologies suffer from fundamental flaws such as a strong device-to-device and cycle-to-cycle variability which is worsened by aging, forcing the designers to consider worst case design conditions. In this work, we propose, for the first time, a circuit that can take advantage of recently published Write Termination (WT) circuits from both the energy and performances point of view. The proposed RRAM Variability Aware Controller (RRAM-VAC) stores and then coalesces the write requests from the processor before triggering the actual write process. By doing so, it averages the RRAM variability and enables the system to run at the memory programming time distribution mean rather than the worst case tail. We explore the design space of the proposed solution for various RRAM variability specifications, benchmark the effect of the proposed memory controller with real application memory traces and show (for the considered RRAM technology specifications) 44 % to 50 % performances improvement and from 10% to 85% energy gains depending on the application memory access patterns.

[1]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[2]  Jean-Philippe Noël,et al.  Architecture, design and technology guidelines for crosspoint memories , 2017, 2017 IEEE/ACM International Symposium on Nanoscale Architectures (NANOARCH).

[3]  Elisa Vianello,et al.  Sub-pJ consumption and short latency time in RRAM arrays for high endurance applications , 2018, 2018 IEEE International Reliability Physics Symposium (IRPS).

[4]  Pulkit Jain,et al.  13.2 A 3.6Mb 10.1Mb/mm2 Embedded Non-Volatile ReRAM Macro in 22nm FinFET Technology with Adaptive Forming/Set/Reset Schemes Yielding Down to 0.5V with Sensing Time of 5ns at 0.7V , 2019, 2019 IEEE International Solid- State Circuits Conference - (ISSCC).

[5]  Shimeng Yu,et al.  Metal–Oxide RRAM , 2012, Proceedings of the IEEE.

[6]  David Atienza,et al.  e-Glass: A Wearable System for Real-Time Detection of Epileptic Seizures , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[7]  Bernard Dieny,et al.  Magnetoresistive Random Access Memory , 2016, Proceedings of the IEEE.

[8]  David Atienza,et al.  TamaRISC-CS: An ultra-low-power application-specific processor for compressed sensing , 2012, 2012 IEEE/IFIP 20th International Conference on VLSI and System-on-Chip (VLSI-SoC).

[9]  M. Breitwisch Phase Change Memory , 2008, 2008 International Interconnect Technology Conference.

[10]  E. Muhr,et al.  Switching Event Detection and Self-Termination Programming Circuit for Energy Efficient ReRAM Memory Arrays , 2019, IEEE Transactions on Circuits and Systems II: Express Briefs.

[11]  Pulkit Jain,et al.  13.3 A 7Mb STT-MRAM in 22FFL FinFET Technology with 4ns Read Sensing Time at 0.9V Using Write-Verify-Write Scheme and Offset-Cancellation Sensing Technique , 2019, 2019 IEEE International Solid- State Circuits Conference - (ISSCC).

[12]  Sanu Mathew,et al.  A 128×128b high-speed wide-and match-line content addressable memory in 32nm CMOS , 2011, 2011 Proceedings of the ESSCIRC (ESSCIRC).

[13]  Olivier Thomas,et al.  Back-end 3D integration of HfO2-based RRAMs for low-voltage advanced IC digital design , 2013, Proceedings of 2013 International Conference on IC Design & Technology (ICICDT).

[14]  David Gregg,et al.  Parallel Multi Channel convolution using General Matrix Multiplication , 2017, 2017 IEEE 28th International Conference on Application-specific Systems, Architectures and Processors (ASAP).