跳到正文
研踪PaperTrail

YOUR RESEARCH, IN SIGHT

让下一篇重要论文,不再错过。

覆盖计算机全学科,以 AI 为快捷入口。关注方向、作者和关键词,把发现变成持续的研究积累。

采集已暂停
正在读取账号与研究记录…

全站更新概况

+0新增论文
+0其中 AI 相关
+0已有论文修订
+0新增 AI 导读

arXiv 最近完成扫描:等待完成 · 全站最近入库:2026-11-02 00:39各信源进度 →

统计口径与数据新鲜度

统计 2026-11-03 09:19 — 2026-11-04 09:19(北京时间) · 点击数字查看对应简报;AI 相关包含在新增论文内。

最近完成检查:尚未完成最近发现新增版本:2026-11-02 00:39库内论文最新更新时间:2026-10-30 01:59

每分钟刷新已保存的数据。空检查不代表采集异常;新收录可能包含历史论文。AI 导读按新增版本计数。

8,677 已收录论文7,675 AI 相关1,866 有修订112 HF 社区发现

浏览论文库

时间范围跟随排序:版本更新、本站收录或首次发表。最新收录可能包含历史论文。

找到 1 篇论文 · 按版本更新时间排序等待首次采集
新论文 · v1arXiv:2610.39337

Versatile Photonic Encoding Platform with Selectable Back Ends for Automotive Edge Processing

Isamu Takai, Makoto Nakai, Ayana Mizuno, Atsutaka Miyamichi

The increasing diversity and computational demands of automotive edge functions require hardware architectures that support heterogeneous processing while limiting implementation complexity and system overhead. We investigate a system-level approach in which a common, fixed photonic front end provides a shared physical transformation for multiple processing functions, while application adaptation is handled by lightweight peripheral processing. We propose the Versatile Photonic Encoding Platform (VPEP), implemented using a fabricated silicon-photonic chip with 32 phase-modulated inputs, a passive fixed encoding core, and 17 on-chip photodetector outputs. Without reconfiguring the photonic core, the same chip is evaluated across four representative functions relevant to automotive edge systems: classification and regression for machine learning, analog-to-digital conversion for mixed-signal processing, and photonic information encoding and recovery for communication-related processing. Compared with linear baselines without photonic encoding, VPEP improves the mean macro-F1 score on 12 of 13 classification datasets and the mean coefficient of determination on 9 of 12 regression benchmarks. For analog-to-digital conversion, the best configuration achieves an in-sample normalized root-mean-square error of approximately 0.75% of full scale. For information encoding and recovery, a lightweight nonlinear decoder reconstructs four-character (32-bit) inputs from 17-channel encoded outputs with approximately 94% character accuracy for a 26-symbol set. These results demonstrate the feasibility of cross-application processing with a single fixed silicon-photonic encoding core and suggest a system-level design approach, complementary to reconfigurable photonic architectures, that combines common photonic hardware with lightweight application-specific processing for future automotive edge systems.

尚无 AI 解读,展示原文摘要

cs.ET
arXiv ↗PDF ↗