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统计口径与数据新鲜度

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版本更新 · v3arXiv:2504.04404

fRAC: Remote Accelerator Calls on Hostless FPGAs

Ziyi Yang, Krishnan B. Iyer, Yixi Chen, Ran Shu, Zsolt István, Marco Canini, Suhaib A. Fahmy

原文摘要

Hardware accelerators are essential for meeting microsecond-scale demands in distributed applications, but invoking them over the network remains bottlenecked by host software. Host-mediated paths (RPC, RDMA) still rely on the CPU to reassemble requests and launch accelerators, which dominates end-to-end latency, while packet-level offload on SmartNICs lacks any notion of a complete request. We introduce the Remote Accelerator Call (RAC) paradigm, in which transport ingestion, request reassembly, dispatch, and accelerator invocation are handled entirely in hardware. We build fRAC, a hostless prototype that implements this network-to-accelerator interface as a lightweight shim on an FPGA hosting multiple virtualized accelerators, reconfigurable at runtime in a few milliseconds. Serverless functions backed by fine-grained accelerators are a natural fit, since host-mediated invocation overhead can match or exceed their short execution times. fRAC achieves under 7.8us end-to-end latency while sustaining application throughput of up to 90 Gbps. Compared to highly optimized DPDK-based CPU and DPU baselines, it achieves at least 80% lower latency under saturation, while consuming 85% less power.

cs.NI · cs.AR

首次观测 · 记录 #1ACL Anthology · ACL 2026:acl:2026.acl-long.624ACL Anthology · ACL 2026

Language Model as Planner and Formalizer under Constraints

Cassie Huang Drexel University, Stuti Mohan Drexel University, Ziyi Yang Brown University, Stefanie Tellex, Li Zhang Drexel University

原文摘要

LLMs have been widely used in planning, either as planners to generate action sequences end-to-end, or as formalizers to represent the planning domain and problem in a formal language that can derive plans deterministically. However, both lines of work rely on standard benchmarks that include only generic and simplistic environmental specifications, leading to potential overestimation of the planning ability of LLMs and safety concerns in downstream tasks. We bridge this gap by augmenting widely used planning benchmarks with manually annotated, fine-grained, and rich natural language constraints spanning four formally defined categories. Over 4 state-of-the-art reasoning LLMs, 4 formal languages, and 4 datasets, we show that the introduction of one-sentence constraints consistently halves performance, indicating current LLMs’ lack of robustness and an avenue for future research.

cs.CL

Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表