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UID:dac_DAC 2026_sess315_RESEARCH641@linklings.com
SUMMARY:TorR: Towards Brain-Inspired Task-Oriented Reasoning via Cache-Ori
 ented Algorithm-Architecture Co-design
DESCRIPTION:Hyunwoo Oh (University of California, Irvine); SungHeon Jeong 
 (UCI); Suyeon Jang, Hanning Chen, Sanggeon Yun, and Tamoghno Das (Universi
 ty of California, Irvine); and Mohsen Imani (University of California Irvi
 ne)\n\nTask-oriented object detection on CLIP enables open-vocabulary, pro
 mpt-driven semantics but dense alignment and memory traffic block real-tim
 e edge use. We propose TorR, a brain-inspired algorithm–architecture co-de
 sign that replaces CLIP-style dense matching with hyperdimensional associa
 tive reasoning and exploits temporal coherence. TorR combines HDC similari
 ty, graph reasoning with query caching and delta updates, and a lane-scala
 ble, precision-gated item memory with RT-30/RT-60 control. A 28 nm TorR ac
 celerator delivers real-time detection with millijoule energy and competit
 ive AP at orders-of-magnitude lower energy.\n\nTopics: Quantum\n\nTrack: D
 ES6. Quantum Computing, Systems\n\nSession Chairs: Hokeun Kim (Arizona Sta
 te University); BaekGyu Kim (Daegu Gyeongbuk Institute of Science and Tech
 nology); Xiang Chen (Peking University); Sangyeob Kim (Yonsei University);
  Robert M. Radway (University of Pennsylvania); Prashanth Mohan (Universit
 y of Pittsburgh); Xiaoxuan Yang (University of Virginia, Stanford Universi
 ty); Ramtin Zand (University of South Carolina); Hadi Kamali (University o
 f Central Florida); Jiafeng Xie (Villanova University); Prabuddha Chakrabo
 rty (University of Maine); Junichiro Kadomoto (University of Tokyo); and Y
 osuke Ueno (RIKEN)\n\n
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