A technical paper titled “HYPERPILL: Fuzzing for Hypervisor-bugs by Leveraging the Hardware Virtualization Interface” was presented at the August 2024 USENIX Security Symposium by researchers at EPFL, Boston University, and Zhejiang University.

Abstract:

“The security guarantees of cloud computing depend on the isolation guarantees of the underlying hypervisors. Prior works have presented effective methods for automatically identifying vulnerabilities in hypervisors. However, these approaches are limited in scope. For instance, their implementation is typically hypervisor-specific and limited by requirements for detailed grammars, access to source-code, and assumptions about hypervisor behaviors. In practice, complex closed-source and recent open-source hypervisors are often not suitable for off-the-shelf fuzzing techniques.

HYPERPILL introduces a generic approach for fuzzing arbitrary hypervisors. HYPERPILL leverages the insight that although hypervisor implementations are diverse, all hypervisors rely on the identical underlying hardware-virtualization interface to manage virtual-machines. To take advantage of the hardware-virtualization interface, HYPERPILL makes a snapshot of the hypervisor, inspects the snapshotted hardware state to enumerate the hypervisor’s input-spaces, and leverages feedback-guided snapshot-fuzzing within an emulated environment to identify vulnerabilities in arbitrary hypervisors. In our evaluation, we found that beyond being the first hypervisor-fuzzer capable of identifying vulnerabilities in arbitrary hypervisors across all major attack-surfaces (i.e., PIO/MMIO/Hypercalls/DMA), HYPERPILL also outperforms state-of-the-art approaches that rely on access to source-code, due to the granularity of feedback provided by HYPERPILL’s emulation-based approach. In terms of coverage, HYPERPILL outperformed past fuzzers for 10/12 QEMU devices, without the API hooking or source-code instrumentation techniques required by prior works. HYPERPILL identified 26 new bugs in recent versions of QEMU, Hyper-V, and macOS Virtualization Framework across four device-categories.”

Find the technical paper here. Published August 2024. Distinguished Paper Award Winner.

Bulekov, Alexander, Qiang Liu, Manuel Egele, and Mathias Payer. “HYPERPILL: Fuzzing for Hypervisor-bugs by Leveraging the Hardware Virtualization Interface.” In 33rd USENIX Security Symposium (USENIX Security 24). 2024.

Further Reading
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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).

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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.