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Paul V. Gratz

Publications and source records attributed to Paul V. Gratz.

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Hardware Design and Security in the Era of Chiplets and LLMs

The semiconductor industry is undergoing a dual revolution: the shift toward heterogeneous 2.5D chiplet systems and the integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) flows. While these paradigms offer unprecedented benefits in yield, modularity, design productivity, etc., they radically expand the hardware attack surface. This paper provides a unified analysis of these frontiers, ranging from attacks on chiplet systems (including hardware stacks for LLM acceleration) across architectural, logical, and physical levels, to various exploits against LLM-driven EDA pipelines. To secure chiplet systems, we review a powerful defense approach that leverages 2.5D split manufacturing and active interposers for physically isolated Root of Trust (RoT) architectures. To secure LLM-driven EDA pipelines, we first identify native threats and then review state-of-the-art defense techniques. Finally, we discuss how LLM systems can advance hardware security efforts for modern systems, including chiplets.

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2.5D Root of Trust: Securing the Chiplet Ecosystem

The semiconductor industry is rapidly transitioning from monolithic systems-on-chip toward heterogeneous, multi-vendor 2.5D chiplet ecosystems integrated via silicon interposers. While this paradigm shift offers immense benefits in yield, cost, and time-to-market, it radically expands the attack surface. Integrating chiplets from untrusted foundries and design houses introduces vulnerabilities to hardware Trojans, IP piracy, and system-level communication exploits. Critically, chip-level security features and conventional Root of Trust (RoT) proposals are insufficient in this context: any component, including the interconnect fabric itself, may be sourced from an untrusted vendor. This perspective paper surveys state-of-the-art security strategies for interposer-based 2.5D integration, focusing on three threat categories: interconnect attacks (snooping, spoofing, and man-in-the-middle), cache coherence exploits including complex forging attacks, and microarchitectural side-channel threats. We examine design-time defenses via 2.5D split manufacturing and, more crucially, runtime defenses that establish an active interposer as a physically isolated 2.5D RoT. By embedding so-called transaction monitors and coherence message checkers within the trusted interposer fabric, the system enforces memory access permissions by construction and neutralizes coherence-level attacks without need for modifying/securing the commodity chiplets. Finally, we review the EDA flows required to realize these defenses and show they concurrently improve power and signal integrity while reducing overall system footprint.

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ORAP: Optimized Row Access Prefetching for Rowhammer-mitigated Memory

Rowhammer is a well-studied DRAM phenomenon wherein multiple activations to a given row can cause bit flips in adjacent rows. Many mitigation techniques have been introduced to address Rowhammer, with some support being incorporated into the JEDEC DDR5 standard for per-row-activation-counter (PRAC) and refresh-management (RFM) systems. Mitigation schemes built on these mechanisms claim to have various levels of area, power, and performance overheads. To date the evaluation of existing mitigation schemes typically neglects the impact of other memory system components such as hardware prefetchers. Nearly all modern systems incorporate hardware prefetching and these can significantly improve processor performance through speculative cache population. These prefetchers induce higher numbers of downstream memory requests and increase DRAM activation rates. The performance overhead of Rowhammer mitigations are tied directly to memory access patterns, exposing both hardware prefetchers and Rowhammer mitigations to cross-interaction. We find that the performance improvement provided by prior-work hardware prefetchers is often severely impacted by Rowhammer mitigations. In effect, much of the benefit of speculative memory references from prefetching lies in accelerating and reordering DRAM references in ways that trigger mitigations, significantly reducing the benefits of prefetching. This work proposes the Optimized Row Access Prefetcher (ORAP), leveraging last-level-cache (LLC) space to cache large portions of DRAM rowbuffer contents to reduce the need for future activations. Working with the state-of-the-art Berti prefetcher, ORAP reduces DRAM activation rates by 51.3% and achieves a 4.6% speedup over the prefetcher configuration of Berti and SPP-PPF when prefetching in an RFM-mitigated memory system. Under PRAC mitigations, ORAP reduces energy overheads by 11.8%.

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SPPAM: Signature Pattern Prediction and Access-Map Prefetcher

The discrepancy between processor speed and memory system performance continues to limit the performance of many workloads. To address the issue, one effective and well studied technique is cache prefetching. Many prefetching designs have been proposed, with varying approaches and effectiveness. For example, SPP is a popular prefetcher that leverages confidence throttled recursion to speculate on the future path of program's references, however it is very susceptible to the reference reordering of higher-level caches and the out-of-order core. Orthogonally, AMPM is another popular approach to prefetching which uses reordering-resistant access maps to identify patterns within a region, but is unable to speculate beyond that region. In this paper, we propose SPPAM, a new approach to prefetching, inspired by prior works such as SPP and AMPM, while addressing their limitations. SPPAM utilizes online-learning to build a set of access-map patterns. These patterns are used in a speculative lookahead which is throttled by a confidence metric. Targeting the second-level cache, SPPAM alongside state-of-the-art prefetchers Berti and Bingo improves system performance by 31.4% over no prefetching and 6.2% over the baseline of Berti and Pythia.

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Targeted Wearout Attacks in Microprocessor Cores

Negative-Bias Temperature Instability is a dominant aging mechanism in nanoscale CMOS circuits such as microprocessors. With this aging mechanism, the rate of device aging is dependent not only on overall operating conditions, such as heat, but also on user controllable inputs to the transistors. This dependence on input implies a possible timing fault-injection attack wherein a targeted path of logic is intentionally degraded through the purposeful, software-driven actions of an attacker, rendering a targeted bit effectively stuck. In this work, we describe such an attack mechanism, which we dub a "$\textbf{Targeted Wearout Attack}$", wherein an attacker with sufficient knowledge of the processor core, executing a carefully crafted software program with only user privilege, is able to degrade a functional unit within the processor with the aim of eliciting a particular desired incorrect calculation in a victim application. Here we give a general methodology for the attack. We then demonstrate a case study where a targeted path within the fused multiply-add pipeline in a RISC-V CPU sees a $>7x$ increase in wear over time than would be experienced under typical workloads. We show that an attacker could leverage such an attack, leading to targeted and silent data corruption in a co-running victim application using the same unit.

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Exposing Shadow Branches

Modern processors implement a decoupled front-end in the form of Fetch Directed Instruction Prefetching (FDIP) to avoid front-end stalls. FDIP is driven by the Branch Prediction Unit (BPU), relying on the BPU's accuracy and branch target tracking structures to speculatively fetch instructions into the Instruction Cache (L1I). As data center applications become more complex, their code footprints also grow, resulting in an increase in Branch Target Buffer (BTB) misses. FDIP can alleviate L1I cache misses, but when it encounters a BTB miss, the BPU may not identify the current instruction as a branch to FDIP. This can prevent FDIP from prefetching or cause it to speculate down the wrong path, further polluting the L1I cache. We observe that the vast majority, 75%, of BTB-missing, unidentified branches are actually present in instruction cache lines that FDIP has previously fetched but, these missing branches have not yet been decoded and inserted into the BTB. This is because the instruction line is decoded from an entry point (which is the target of the previous taken branch) till an exit point (the taken branch). Branch instructions present in the ignored portion of the cache line we call them "Shadow Branches". Here we present Skeia, a novel shadow branch decoding technique that identifies and decodes unused bytes in cache lines fetched by FDIP, inserting them into a Shadow Branch Buffer (SBB). The SBB is accessed in parallel with the BTB, allowing FDIP to speculate despite a BTB miss. With a minimal storage state of 12.25KB, Skeia delivers a geomean speedup of ~5.7% over an 8K-entry BTB (78KB) and ~2% versus adding an equal amount of state to the BTB across 16 front-end bound applications. Since many branches stored in the SBB are unique compared to those in a similarly sized BTB, we consistently observe greater performance gains with Skeia across all examined sizes until saturation.

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Correct Wrong Path

Modern OOO CPUs have very deep pipelines with large branch misprediction recovery penalties. Speculatively executed instructions on the wrong path can significantly change cache state, depending on speculation levels. Architects often employ trace-driven simulation models in the design exploration stage, which sacrifice precision for speed. Trace-driven simulators are orders of magnitude faster than execution-driven models, reducing the often hundreds of thousands of simulation hours needed to explore new micro-architectural ideas. Despite this strong benefit of trace-driven simulation, these often fail to adequately model the consequences of wrong path because obtaining them is nontrivial. Prior works consider either a positive or negative impact of wrong path but not both. Here, we examine wrong path execution in simulation results and design a set of infrastructure for enabling wrong-path execution in a trace driven simulator. Our analysis shows the wrong path affects structures on both the instruction and data sides extensively, resulting in performance variations ranging from $-3.05$\% to $20.9$\% when ignoring wrong path. To benefit the research community and enhance the accuracy of simulators, we opened our traces and tracing utility in the hopes that industry can provide wrong-path traces generated by their internal simulators, enabling academic simulation without exposing industry IP.

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The Championship Simulator: Architectural Simulation for Education and Competition

Recent years have seen a dramatic increase in the microarchitectural complexity of processors. This increase in complexity presents a twofold challenge for the field of computer architecture. First, no individual architect can fully comprehend the complexity of the entire microarchitecture of the core. This leads to increasingly specialized architects, who treat parts of the core outside their particular expertise as black boxes. Second, with increasing complexity, the field becomes decreasingly accessible to new students of the field. When learning core microarchitecture, new students must first learn the big picture of how the system works in order to understand how the pieces all fit together. The tools used to study microarchitecture experience a similar struggle. As with the microarchitectures they simulate, an increase in complexity reduces accessibility to new users. In this work, we present ChampSim. ChampSim uses a modular design and configurable structure to achieve a low barrier to entry into the field of microarchitecural simulation. ChampSim has shown itself to be useful in multiple areas of research, competition, and education. In this way, we seek to promote access and inclusion despite the increasing complexity of the field of computer architecture.

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Hardware Trojan Threats to Cache Coherence in Modern 2.5D Chiplet Systems

As industry moves toward chiplet-based designs, the insertion of hardware Trojans poses a significant threat to the security of these systems. These systems rely heavily on cache coherence for coherent data communication, making coherence an attractive target. Critically, unlike prior work, which focuses only on malicious packet modifications, a Trojan attack that exploits coherence can modify data in memory that was never touched and is not owned by the chiplet which contains the Trojan. Further, the Trojan need not even be physically between the victim and the memory controller to attack the victim's memory transactions. Here, we explore the fundamental attack vectors possible in chiplet-based systems and provide an example Trojan implementation capable of directly modifying victim data in memory. This work aims to highlight the need for developing mechanisms that can protect and secure the coherence scheme from these forms of attacks.

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FPGA-based Hyrbid Memory Emulation System

Hybrid memory systems, comprised of emerging non-volatile memory (NVM) and DRAM, have been proposed to address the growing memory demand of applications. Emerging NVM technologies, such as phase-change memories (PCM), memristor, and 3D XPoint, have higher capacity density, minimal static power consumption and lower cost per GB. However, NVM has longer access latency and limited write endurance as opposed to DRAM. The different characteristics of two memory classes point towards the design of hybrid memory systems containing multiple classes of main memory. In the iterative and incremental development of new architectures, the timeliness of simulation completion is critical to project progression. Hence, a highly efficient simulation method is needed to evaluate the performance of different hybrid memory system designs. Design exploration for hybrid memory systems is challenging, because it requires emulation of the full system stack, including the OS, memory controller, and interconnect. Moreover, benchmark applications for memory performance test typically have much larger working sets, thus taking even longer simulation warm-up period. In this paper, we propose a FPGA-based hybrid memory system emulation platform. We target at the mobile computing system, which is sensitive to energy consumption and is likely to adopt NVM for its power efficiency. Here, because the focus of our platform is on the design of the hybrid memory system, we leverage the on-board hard IP ARM processors to both improve simulation performance while improving accuracy of the results. Thus, users can implement their data placement/migration policies with the FPGA logic elements and evaluate new designs quickly and effectively. Results show that our emulation platform provides a speedup of 9280x in simulation time compared to the software counterpart Gem5.

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