Understanding Reduced-Voltage Operation in Modern DRAM Chips: Characterization, Analysis, and Mechanisms
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Abstract (arXiv)
The energy consumption of DRAM is a critical concern in modern computing systems. Improvements in manufacturing process technology have allowed DRAM vendors to lower the DRAM supply voltage conservatively, which reduces some of the DRAM energy consumption. We would like to reduce the DRAM supply voltage more aggressively, to further reduce energy. Aggressive supply voltage reduction requires a thorough understanding of the effect voltage scaling has on DRAM access latency and DRAM reliability. In this paper, we take a comprehensive approach to understanding and exploiting the latency and reliability characteristics of modern DRAM when the supply voltage is lowered below the nominal voltage level specified by DRAM standards. Using an FPGA-based testing platform, we perform an experimental study of 124 real DDR3L (low-voltage) DRAM chips manufactured recently by three major DRAM vendors. We find that reducing the supply voltage below a certain point introduces bit errors in the data, and we comprehensively characterize the behavior of these errors. We discover that these errors can be avoided by increasing the latency of three major DRAM operations (activation, restoration, and precharge). We perform detailed DRAM circuit simulations to validate and explain our experimental findings. We also characterize the various relationships between reduced supply voltage and error locations, stored data patterns, DRAM temperature, and data retention. Based on our observations, we propose a new DRAM energy reduction mechanism, called Voltron. The key idea of Voltron is to use a performance model to determine by how much we can reduce the supply voltage without introducing errors and without exceeding a user-specified threshold for performance loss. Voltron reduces the average system energy by 7.3% while limiting the average system performance loss to only 1.8%, for a variety of workloads.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 May 2017.
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Citing papers per year: 2021: 3; 2022: 0; 2023: 1.
4 papers.
- 2021ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memoriescited by 20
- 2021Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Acceleratorscited by 6
- 2023EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systemscited by 6
- 2021Using Undervolting as an On-Device Defense Against Adversarial Machine Learning Attackscited by 1
- 2023EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems8 Apr 2023
- 2021ReSpawn: Energy-Efficient Fault-Tolerance for Spiking Neural Networks considering Unreliable Memories23 Aug 2021
- 2021Using Undervolting as an On-Device Defense Against Adversarial Machine Learning Attacks20 Jul 2021
- 2021Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators16 Apr 2021