arXiv subjects

Binqi Sun

Publications and source records attributed to Binqi Sun.

7 recordsLinked to original sources

arXiv2026

EnerInfer: Energy-Aware On-Device LLM Inference

On-device LLM inference is increasingly attractive for privacy-preserving, reliable, and cost-effective deployment, yet its energy and thermal costs remain a critical bottleneck. Existing systems primarily optimize for decoding speed, implicitly assuming that faster execution is always preferable. We show instead that on-device LLM inference often has exploitable configuration slack: modestly lowering NPU and memory frequencies preserves quality of experience (QoE) while substantially improving energy efficiency and reducing heat. Realizing this opportunity in production is challenging. The most energy-efficient NPU/DDR setting varies with the model, inference engine, platform, and runtime conditions, with no stable ranking across configurations. Commercial devices further lack component-level power sensing, and shell temperature evolves with request arrivals, response lengths, and thermal history. To address these challenges, we propose EnerInfer, the first on-device LLM inference framework that jointly manages energy efficiency, throughput, and thermal comfort for LLM workloads. EnerInfer replaces per-model profiling and sensor-heavy control with disaggregated, model-structure-aware prediction and ranking-driven online feedback. It predicts throughput and power for unseen LLMs across NPU/DDR frequency settings, selects QoE-satisfying efficient configurations under runtime interference, and uses lightweight limited-horizon thermal prediction to dynamically switch between energy-optimized and thermally constrained inference. Evaluations on real-world LLMs show that EnerInfer improves energy efficiency by up to 65%, 12%, and 24% on phones, a laptop, and a development board, respectively, without QoE violation.

cs.SE↗

arXiv2026

ProfInfer: An eBPF-based Fine-Grained LLM Inference Profiler

As large language models (LLMs) move from research to production, understanding how inference engines behave in real time has become both essential and elusive. Unlike general-purpose engines such as ONNX Runtime, today's LLM inference systems offer little operator-level visibility, leaving developers blind to where time and resources go. Even basic questions -- is this workload memory-bound or compute-bound? -- often remain unanswered. To close this gap, we develop a fine-grained, non-intrusive profiling framework for modern LLM inference engines, exemplified by llama-cpp but applicable to similar runtime architectures. Built on extended Berkeley Packet Filter (eBPF) technology, our system dynamically attaches probes to runtime functions across multiple layers -- without modifying or recompiling the source. It transforms collected traces into rich visualizations of operators, graphs, timelines, and hardware counter trends, exposing how dense inference, Mixture-of-Experts routing, and operator offloading behave in practice. With less than 4% runtime overhead and high profiling fidelity, our framework makes LLM inference both transparent and diagnosable, turning performance profiling into a practical tool for optimization, scheduling, and resource-aware deployment.

cs.SE↗

arXiv2025

Multi-Objective Memory Bandwidth Regulation and Cache Partitioning for Multicore Real-Time Systems

Memory bandwidth regulation and cache partitioning are widely used techniques for achieving predictable timing in real-time computing systems. Combined with partitioned scheduling, these methods require careful co-allocation of tasks and resources to cores, as task execution times strongly depend on available allocated resources. To address this challenge, this paper presents a 0-1 linear program for task-resource co-allocation, along with a multi-objective heuristic designed to minimize resource usage while guaranteeing schedulability under a preemptive EDF scheduling policy. Our heuristic employs a multi-layer framework, where an outer layer explores resource allocations using Pareto-pruned search, and an inner layer optimizes task allocation by solving a knapsack problem using dynamic programming. To evaluate the performance of the proposed optimization algorithm, we profile real-world benchmarks on an embedded AMD UltraScale+ ZCU102 platform, with fine-grained resource partitioning enabled by the Jailhouse hypervisor, leveraging cache set partitioning and MemGuard for memory bandwidth regulation. Experiments based on the benchmarking results show that the proposed 0-1 linear program outperforms existing mixed-integer programs by finding more optimal solutions within the same time limit. Moreover, the proposed multi-objective multi-layer heuristic performs consistently better than the state-of-the-art multi-resource-task co-allocation algorithm in terms of schedulability, resource usage, number of non-dominated solutions, and computational efficiency.

math.OC↗

arXiv2024

PretVM: Predictable, Efficient Virtual Machine for Real-Time Concurrency

This paper introduces the Precision-Timed Virtual Machine (PretVM), an intermediate platform facilitating the execution of quasi-static schedules compiled from a subset of programs written in the Lingua Franca (LF) coordination language. The subset consists of those programs that in principle should have statically verifiable and predictable timing behavior. The PretVM provides a schedule with well-defined worst-case timing bounds. The PretVM provides a clean separation between application logic and coordination logic, yielding more analyzable program executions. Experiments compare the PretVM against the default (more dynamic) LF scheduler and show that it delivers time-accurate deterministic execution.

eess.SY↗

arXiv2024

Strict Partitioning for Sporadic Rigid Gang Tasks

The rigid gang task model is based on the idea of executing multiple threads simultaneously on a fixed number of processors to increase efficiency and performance. Although there is extensive literature on global rigid gang scheduling, partitioned approaches have several practical advantages (e.g., task isolation and reduced scheduling overheads). In this paper, we propose a new partitioned scheduling strategy for rigid gang tasks, named strict partitioning. The method creates disjoint partitions of tasks and processors to avoid inter-partition interference. Moreover, it tries to assign tasks with similar volumes (i.e., parallelisms) to the same partition so that the intra-partition interference can be reduced. Within each partition, the tasks can be scheduled using any type of scheduler, which allows the use of a less pessimistic schedulability test. Extensive synthetic experiments and a case study based on Edge TPU benchmarks show that strict partitioning achieves better schedulability performance than state-of-the-art global gang schedulability analyses for both preemptive and non-preemptive rigid gang task sets.

cs.DC↗

arXiv2023

Co-Optimizing Cache Partitioning and Multi-Core Task Scheduling: Exploit Cache Sensitivity or Not?

Cache partitioning techniques have been successfully adopted to mitigate interference among concurrently executing real-time tasks on multi-core processors. Considering that the execution time of a cache-sensitive task strongly depends on the cache available for it to use, co-optimizing cache partitioning and task allocation improves the system's schedulability. In this paper, we propose a hybrid multi-layer design space exploration technique to solve this multi-resource management problem. We explore the interplay between cache partitioning and schedulability by systematically interleaving three optimization layers, viz., (i) in the outer layer, we perform a breadth-first search combined with proactive pruning for cache partitioning; (ii) in the middle layer, we exploit a first-fit heuristic for allocating tasks to cores; and (iii) in the inner layer, we use the well-known recurrence relation for the schedulability analysis of non-preemptive fixed-priority (NP-FP) tasks in a uniprocessor setting. Although our focus is on NP-FP scheduling, we evaluate the flexibility of our framework in supporting different scheduling policies (NP-EDF, P-EDF) by plugging in appropriate analysis methods in the inner layer. Experiments show that, compared to the state-of-the-art techniques, the proposed framework can improve the real-time schedulability of NP-FP task sets by an average of 15.2% with a maximum improvement of 233.6% (when tasks are highly cache-sensitive) and a minimum of 1.6% (when cache sensitivity is low). For such task sets, we found that clustering similar-period (or mutually compatible) tasks often leads to higher schedulability (on average 7.6%) than clustering by cache sensitivity. In our evaluation, the framework also achieves good results for preemptive and dynamic-priority scheduling policies.

cs.AR↗

arXiv2023

Edge Generation Scheduling for DAG Tasks Using Deep Reinforcement Learning

Directed acyclic graph (DAG) tasks are currently adopted in the real-time domain to model complex applications from the automotive, avionics, and industrial domains that implement their functionalities through chains of intercommunicating tasks. This paper studies the problem of scheduling real-time DAG tasks by presenting a novel schedulability test based on the concept of trivial schedulability. Using this schedulability test, we propose a new DAG scheduling framework (edge generation scheduling -- EGS) that attempts to minimize the DAG width by iteratively generating edges while guaranteeing the deadline constraint. We study how to efficiently solve the problem of generating edges by developing a deep reinforcement learning algorithm combined with a graph representation neural network to learn an efficient edge generation policy for EGS. We evaluate the effectiveness of the proposed algorithm by comparing it with state-of-the-art DAG scheduling heuristics and an optimal mixed-integer linear programming baseline. Experimental results show that the proposed algorithm outperforms the state-of-the-art by requiring fewer processors to schedule the same DAG tasks. The code is available at https://github.com/binqi-sun/egs.

cs.LG↗