10 papers
This paper proposes a task-based scheduling framework that ensures end-to-end data freshness in safety-critical multi-rate systems by introducing a Consensus Offset Search algorithm to align task releases with data lifespan constraints, thereby eliminating the artificial latency of Logical Execution Time and the inefficiency of redundant oversampling while preserving Global EDF schedulability.
This paper presents FlexServe, a high-performance and secure LLM serving system for mobile devices that leverages a novel Flexible Resource Isolation mechanism to overcome the significant overhead of ARM TrustZone, achieving up to 10.05× faster time-to-first-token and 24.30× faster multi-model workflow execution compared to baseline designs.
This paper introduces Pichay, a demand paging system that treats LLM context windows as a memory hierarchy rather than a static cache, successfully reducing context consumption by up to 93% in production by evicting stale content and dynamically reloading it only when needed.
This paper presents a novel capability architecture and a corresponding Zephyr-based real-time operating system that achieves comprehensive security for embedded devices by fully disaggregating and isolating all software subsystems and peripherals, thereby eliminating the need for a run-time software Trusted Computing Base (TCB) without requiring hardware modifications.
This paper introduces Structured Gossip DNS, a partition-resilient name resolution system for large-scale dynamic networks that leverages DHT finger tables and passive stabilization to achieve eventual consistency with reduced message complexity and without requiring global coordination.
This paper demonstrates that leveraging eBPF-based system-level monitoring reveals new leakage patterns in Searchable Symmetric Encryption (SSE) that extend beyond traditional threat models, thereby enabling more powerful leakage abuse attacks and highlighting the critical need to address system-level exposures in SSE defenses.
This paper presents EROICA, the first online troubleshooting system deployed on production-scale GPU clusters (~100,000 GPUs) that effectively diagnoses complex hardware and software performance issues in large-scale model training through fine-grained profiling and differential observability with minimal impact.
This dissertation addresses the memory bottleneck in modern computing by advocating a shift from data-agnostic to data-informed microarchitectural designs, proposing four machine learning-driven and data-aware mechanisms that significantly enhance performance and energy efficiency.
This paper argues that achieving high-performance end-to-end data movement requires shifting focus from raw network bandwidth to a holistic hardware-software co-design approach, introducing the "Drainage Basin Pattern" to identify and resolve bottlenecks across six critical paradigms ranging from network latency to host-side factors.
This paper presents empirical results from a production-grade C++ implementation of the Compute ICE-AGE, a deterministic semantic state substrate that achieves invariant traversal latency and thermodynamic stability by evolving a persistent addressable memory graph under bounded local operators, thereby decoupling compute costs from token volume and context horizon.