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  • ✇Semiconductor Engineering
  • NeuroHammer Attacks on ReRAM-Based MemoriesTechnical Paper Link
    A new technical paper titled “NVM-Flip: Non-Volatile-Memory BitFlips on the System Level” was published by researchers at Ruhr-University Bochum, University of Duisburg-Essen, and Robert Bosch. Abstract “Emerging non-volatile memories (NVMs) are promising candidates to substitute conventional memories due to their low access latency, high integration density, and non-volatility. These superior properties stem from the memristor representing the centerpiece of each memory cell and is branded as t
     

NeuroHammer Attacks on ReRAM-Based Memories

21. Červen 2024 v 18:32

A new technical paper titled “NVM-Flip: Non-Volatile-Memory BitFlips on the System Level” was published by researchers at Ruhr-University Bochum, University of Duisburg-Essen, and Robert Bosch.

Abstract
“Emerging non-volatile memories (NVMs) are promising candidates to substitute conventional memories due to their low access latency, high integration density, and non-volatility. These superior properties stem from the memristor representing the centerpiece of each memory cell and is branded as the fourth fundamental circuit element. Memristors encode information in the form of its resistance by altering the physical characteristics of their filament. Hence, each memristor can store multiple bits increasing the memory density and positioning it as a potential candidate to replace DRAM and SRAM-based memories, such as caches.

However, new security risks arise with the benefits of these emerging technologies, like the recent NeuroHammer attack, which allows adversaries to deliberately flip bits in ReRAMs. While NeuroHammer has been shown to flip single bits within memristive crossbar arrays, the system-level impact remains unclear. Considering the significance of the Rowhammer attack on conventional DRAMs, NeuroHammer can potentially cause crucial damage to applications taking advantage of emerging memory technologies.

To answer this question, we introduce NVgem5, a versatile system-level simulator based on gem5. NVgem5 is capable of injecting bit-flips in eNVMs originating from NeuroHammer. Our experiments evaluate the impact of the NeuroHammer attack on main and cache memories. In particular, we demonstrate a single-bit fault attack on cache memories leaking the secret key used during the computation of RSA signatures. Our findings highlight the need for improved hardware security measures to mitigate the risk of hardware-level attacks in computing systems based on eNVMs.”

Find the technical paper here. Published June 2024.

Felix Staudigl, Jan Philipp Thoma, Christian Niesler, Karl Sturm, Rebecca Pelke, Dominik Germek, Jan Moritz Joseph, Tim Güneysu, Lucas Davi, and Rainer Leupers. 2024. NVM-Flip: Non-Volatile-Memory BitFlips on the System Level. In Proceedings of the 2024 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems (SaT-CPS ’24). Association for Computing Machinery, New York, NY, USA, 11–20. https://doi.org/10.1145/3643650.3658606

The post NeuroHammer Attacks on ReRAM-Based Memories appeared first on Semiconductor Engineering.

  • ✇Semiconductor Engineering
  • Ultra-Low Power CiM Design For Practical Edge ScenariosTechnical Paper Link
    A technical paper titled “Low Power and Temperature-Resilient Compute-In-Memory Based on Subthreshold-FeFET” was published by researchers at Zhejiang University, University of Notre Dame, Technical University of Munich, Munich Institute of Robotics and Machine Intelligence, and the Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province. Abstract: “Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and th
     

Ultra-Low Power CiM Design For Practical Edge Scenarios

A technical paper titled “Low Power and Temperature-Resilient Compute-In-Memory Based on Subthreshold-FeFET” was published by researchers at Zhejiang University, University of Notre Dame, Technical University of Munich, Munich Institute of Robotics and Machine Intelligence, and the Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province.

Abstract:

“Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial intelligence (AI) and the Internet of Things (IoT) hardware such as ‘memory wall’ issue. Specifically, CiM employing nonvolatile memory (NVM) devices in a crossbar structure can efficiently accelerate multiply-accumulation (MAC) computation, a crucial operator in neural networks among various AI models. Low power CiM designs are thus highly desired for further energy efficiency optimization on AI models. Ferroelectric FET (FeFET), an emerging device, is attractive for building ultra-low power CiM array due to CMOS compatibility, high ION /IOF  ratio, etc. Recent studies have explored FeFET based CiM designs that achieve low power consumption. Nevertheless, subthreshold-operated FeFETs, where the operating voltages are scaled down to the subthreshold region to reduce array power consumption, are particularly vulnerable to temperature drift, leading to accuracy degradation. To address this challenge, we propose a temperature-resilient 2T-1FeFET CiM design that performs MAC operations reliably at subthreahold region from 0 to 85 Celsius, while consuming ultra-low power. Benchmarked against the VGG neural network architecture running the CIFAR-10 dataset, the proposed 2T-1FeFET CiM design achieves 89.45% CIFAR-10 test accuracy. Compared to previous FeFET based CiM designs, it exhibits immunity to temperature drift at an 8-bit wordlength scale, and achieves better energy efficiency with 2866 TOPS/W.”

Find the technical paper here. Published January 2024 (preprint).

Zhou, Yifei, Xuchu Huang, Jianyi Yang, Kai Ni, Hussam Amrouch, Cheng Zhuo, and Xunxhao Yin. “Low Power and Temperature-Resilient Compute-In-Memory Based on Subthreshold-FeFET.” arXiv preprint arXiv:2312.17442 (2023).

Related Reading
Increasing AI Energy Efficiency With Compute In Memory
How to process zettascale workloads and stay within a fixed power budget.
Modeling Compute In Memory With Biological Efficiency
Generative AI forces chipmakers to use compute resources more intelligently.

The post Ultra-Low Power CiM Design For Practical Edge Scenarios appeared first on Semiconductor Engineering.

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