简介：
彭麓洁，算力微电子学院教研助理教授、博士生导师。2020年于电子科技大学与英国格拉斯哥大学获得电子与电气工程专业双学士学位（格拉斯哥大学一等荣誉学位）；2025年于电子科技大学获得博士学位，师从长江学者周军教授；2023年11月至2024年11月在新加坡南洋理工大学开展博士联合培养，并获国家教育部博士生学术支持计划资助。
彭麓洁博士的研究聚焦于低功耗、高准确率的端侧声音智能感知芯片与系统，强调“算法-架构-电路”协同优化与可重构设计方法，致力于构建从算法到硬件可落地的高准确率、低功耗与低延时的完整声音事件检测系统。其研究工作重点围绕三大方向展开：（1）面向音频信号的超低能耗AI SoC，聚焦低功耗声音事件检测任务，面向资源受限的端侧设备，研究高能效、可重构的声音感知芯片架构与系统设计方法；（2）多模态环境感知与智能体听觉系统，面向智能看护、智能安防与具身智能体应用，探索声音在复杂环境感知中的核心作用，构建以听觉为关键补充的多模态感知与智能交互系统；（3）基于存算一体的低功耗智能感知硬件，面向下一代端侧智能计算需求，研究存算一体等新型计算范式在声音及生理信号感知中的应用。
彭麓洁博士作为第一作者在CICC、IEEE TCAS-I等集成电路与系统领域顶级会议和期刊发表学术论文共7篇。受邀担任多个高水平国际期刊和会议评审，包括ASSCC、ISCAS、IEEE TCAS-I、IEEE TVLSI、IEEE TASLP、ICASSP、Interspeech等。同时，作为技术骨干参与国家自然科学基金联合基金重点项目与国家重点研发计划项目。其研究成果兼顾理论创新与工程可实现性，获得IEEE CAS Student Travel Grant等奖项，并带队在全国FPGA创新设计竞赛、全国研究生电子设计竞赛等赛事中获得全国总决赛二等奖。
现面向具有强烈科研热情的优秀人才（博士生、硕士生、博士后、科研助理及实习生）开放招募！欢迎对低功耗AI芯片、环境声理解、多模态感知处理器、存算一体与可重构加速方向有强烈兴趣与科研热情的同学投递简历，共同推进面向真实复杂场景的端到端低功耗智能感知系统落地，让设备真正拥有“听觉”能力，为机器人插上耳朵！
简历投递：penglujie@suat-sz.edu.cn
Lujie Peng is an Assistant Professor and Ph.D. Supervisor at the Faculty of Computility Microelectronics in Shenzhen University of Advanced Technology (SUAT), China.She received her dual B.Eng. degree from University of Electronic Science and Technology of China(UESTC) & University of Glasgow(UofG) in 2020, and the Ph.D. degree from UESTC in 2025 under the supervision of Prof. Jun Zhou. From 2023 to 2024, she was a visiting Ph.D. researcher at Nanyang Technological University(NTU), supported by a Ministry of Education fellowship.
Her research focuses on low-power, high-accuracy edge AI chips for sound intelligence, emphasizing ‘algorithm–architecture–circuit’ co-optimization and reconfigurable design methodologies. She aims to develop fully deployable sound event detection systems that achieve high accuracy, low power consumption, and low latency in real-world environments. Her research interests include ultra-low-energy audio AI SoCs, multimodal environmental perception and auditory systems for intelligent agents, and compute-in-memory(CIM)-based hardware for energy-efficient intelligent sensing.
She is actively recruiting highly motivated Ph.D. students, master’s students, postdoctoral researchers, research assistants, and interns. Applicants with strong interests in low-power AI chips, environmental sound recognition, multimodal perception processors, CIM, and reconfigurable architecture design are welcome to apply. Together, the group aims to advance end-to-end low-power intelligent sensing systems for real-world complex environments—bringing true auditory perception to machines.
