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1. Bridging Hardware and Software Through Causality Inference

   Liu, Zhaoxiang; Chen, Kejun; Sullivan, Dean; Arias, Orlando; Guo, Xiaolong (April 2024, New England Hardware Security Days)

   We propose Microscope, a new framework that addresses growing security issues in System-on-Chip (SoC) designs due to their complexity and involvement of third-party vendors. Traditional methods are inadequate for identifying software-exploited hardware vulnerabilities, and existing solutions for hardware-software co-verification often fall short. The framework has been proven effective through extensive testing on SoC benchmarks, and it has outperformed existing methods and commercial tools in comparative analyses. Index Terms—Causality Inference, Hardware Security, 

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2. Microscope: Causality Inference Crossing the Hardware and Software Boundary from Hardware Perspective

   Liu, Zhaoxiang; Chen, Kejun; Sullivan, Dean; Arias, Orlando; Dutta, Raj Gautam; Jin, Yier; Guo, Xiaolong (April 2024, Proceedings of the ASPDAC Asia and South Pacific Design Automation Conference)

   The increasing complexity of System-on-Chip (SoC) designs and the rise of third-party vendors in the semiconductor industry have led to unprecedented security concerns. Traditional formal methods struggle to address software-exploited hardware bugs, and existing solutions for hardware-software co-verification often fall short. This paper presents Microscope, a novel framework for inferring software instruction patterns that can trigger hardware vulnerabilities in SoC designs. Microscope enhances the Structural Causal Model (SCM) with hardware features, creating a scalable Hardware Structural Causal Model (HW-SCM). A domain-specific language (DSL) in SMT-LIB represents the HW-SCM and predefined security properties, with incremental SMT solving deducing possible instructions. Microscope identifies causality to determine whether a hardware threat could result from any software events, providing a valuable resource for patching hardware bugs and generating test input. Extensive experimentation demonstrates Microscope's capability to infer the causality of a wide range of vulnerabilities and bugs located in SoC-level benchmarks. 

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3. Gables: A Roofline Model for Mobile SoCs

   https://doi.org/10.1109/HPCA.2019.00047  

   Hill, Mark; Janapa Reddi, Vijay (February 2019, 2019 IEEE International Symposium on High Performance Computer Architecture (HPCA))

   Over a billion mobile consumer system-on-chip (SoC) chipsets ship each year. Of these, the mobile consumer market undoubtedly involving smartphones has a significant market share. Most modern smartphones comprise of advanced SoC architectures that are made up of multiple cores, GPS, and many different programmable and fixed-function accelerators connected via a complex hierarchy of interconnects with the goal of running a dozen or more critical software usecases under strict power, thermal and energy constraints. The steadily growing complexity of a modern SoC challenges hardware computer architects on how best to do early stage ideation. Late SoC design typically relies on detailed full-system simulation once the hardware is specified and accelerator software is written or ported. However, early-stage SoC design must often select accelerators before a single line of software is written. To help frame SoC thinking and guide early stage mobile SoC design, in this paper we contribute the Gables model that refines and retargets the Roofline model-designed originally for the performance and bandwidth limits of a multicore chip-to model each accelerator on a SoC, to apportion work concurrently among different accelerators (justified by our usecase analysis), and calculate a SoC performance upper bound. We evaluate the Gables model with an existing SoC and develop several extensions that allow Gables to inform early stage mobile SoC design. 

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4. SpikeHard: Efficiency-Driven Neuromorphic Hardware for Heterogeneous Systems-on-Chip

   https://doi.org/10.1145/3609101  

   Clair, Judicael; Eichler, Guy; Carloni, Luca P (October 2023, ACM Transactions on Embedded Computing Systems)

   Neuromorphic computing is an emerging field with the potential to offer performance and energy-efficiency gains over traditional machine learning approaches. Most neuromorphic hardware, however, has been designed with limited concerns to the problem of integrating it with other components in a heterogeneous System-on-Chip (SoC). Building on a state-of-the-art reconfigurable neuromorphic architecture, we present the design of a neuromorphic hardware accelerator equipped with a programmable interface that simplifies both the integration into an SoC and communication with the processor present on the SoC. To optimize the allocation of on-chip resources, we develop an optimizer to restructure existing neuromorphic models for a given hardware architecture, and perform design-space exploration to find highly efficient implementations. We conduct experiments with various FPGA-based prototypes of many-accelerator SoCs, where Linux-based applications running on a RISC-V processor invoke Pareto-optimal implementations of our accelerator alongside third-party accelerators. These experiments demonstrate that our neuromorphic hardware, which is up to 89× faster and 170× more energy efficient after applying our optimizer, can be used in synergy with other accelerators for different application purposes. 

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5. TAAL: Tampering Attack on Any Key-based Logic Locked Circuits

   https://doi.org/10.1145/3442379  

   Jain, Ayush; Zhou, Ziqi; Guin, Ujjwal (March 2021, ACM Transactions on Design Automation of Electronic Systems)

   null (Ed.)

   Due to the globalization of semiconductor manufacturing and test processes, the system-on-a-chip (SoC) designers no longer design the complete SoC and manufacture chips on their own. This outsourcing of the design and manufacturing of Integrated Circuits (ICs) has resulted in several threats, such as overproduction of ICs, sale of out-of-specification/rejected ICs, and piracy of Intellectual Properties (IPs). Logic locking has emerged as a promising defense strategy against these threats. However, various attacks about the extraction of secret keys have undermined the security of logic locking techniques. Over the years, researchers have proposed different techniques to prevent existing attacks. In this article, we propose a novel attack that can break any logic locking techniques that rely on the stored secret key. This proposed TAAL attack is based on implanting a hardware Trojan in the netlist, which leaks the secret key to an adversary once activated. As an untrusted foundry can extract the netlist of a design from the layout/mask information, it is feasible to implement such a hardware Trojan. All three proposed types of TAAL attacks can be used for extracting secret keys. We have introduced the models for both the combinational and sequential hardware Trojans that evade manufacturing tests. An adversary only needs to choose one hardware Trojan out of a large set of all possible Trojans to launch the TAAL attack. 

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