{"title":"Birch: A Real-Time Multi-Domain Multi-Task Extended Reality Perception Accelerator","authors":"Kathleen Feng;Kai Bartolone;Kartik Prabhu;Brianna McColm;Xingyu Ni;Siddhant Gupta;Priyanka Raina","doi":"10.1109/LSSC.2026.3673168","DOIUrl":null,"url":null,"abstract":"Birch is a system-on-chip (SoC) that efficiently and accurately accelerates the multi-task multi-domain extended reality (XR) perception pipeline, with workloads such as visual inertial odometry (VIO), eye gaze tracking, and scene understanding. Birch features vision modules with cascaded line buffers, in-step feature sorting, and double-buffered optical flow to extract and track quality features for VIO, a configurable matrix unit and vector unit for acceleration of deep neural network (DNN)-based tasks like eye gaze tracking and scene understanding, and reuse of the DNN vector unit for acceleration of matrix multiplications in VIO to save area in small-form-factor XR devices. Birch extracts and tracks features in 7.14 ms and 2.29 mJ/frame. For DNNs, Birch runs ResNet-18 at 109 frames/s and eye gaze extraction at 985 frames/s, <inline-formula> <tex-math>$3.5\\times $ </tex-math></inline-formula> and <inline-formula> <tex-math>$1.6\\times $ </tex-math></inline-formula> faster than prior work, respectively. ResNet-50 also runs in real time at 46.5 frames/s.","PeriodicalId":13032,"journal":{"name":"IEEE Solid-State Circuits Letters","volume":"9 ","pages":"121-124"},"PeriodicalIF":2.0000,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Solid-State Circuits Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/11432884/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/3/12 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
Birch is a system-on-chip (SoC) that efficiently and accurately accelerates the multi-task multi-domain extended reality (XR) perception pipeline, with workloads such as visual inertial odometry (VIO), eye gaze tracking, and scene understanding. Birch features vision modules with cascaded line buffers, in-step feature sorting, and double-buffered optical flow to extract and track quality features for VIO, a configurable matrix unit and vector unit for acceleration of deep neural network (DNN)-based tasks like eye gaze tracking and scene understanding, and reuse of the DNN vector unit for acceleration of matrix multiplications in VIO to save area in small-form-factor XR devices. Birch extracts and tracks features in 7.14 ms and 2.29 mJ/frame. For DNNs, Birch runs ResNet-18 at 109 frames/s and eye gaze extraction at 985 frames/s, $3.5\times $ and $1.6\times $ faster than prior work, respectively. ResNet-50 also runs in real time at 46.5 frames/s.