⚡ 本页包含 AI 生成的分析内容，仅供参考
该论文提出了一款针对微型监视机器人的40nm VLIW边缘加速器，集成了5MB的0.256pJ/b RRAM和定位求解器，解决了感知和定位工作负载的高效计算问题。通过非易失性存储和专用架构实现了超低功耗和实时处理。
Sigang Ryu1, Jong-Hyeok Yoon2, Zhijian Hao1, Azadeh Ansari1, Win-San Khwa3, Yu-Der Chih4, Meng-Fan Chang3, Arijit Raychowdhury1 Georgia Institute of Technology, Atlanta, GA Daegu Gyeongbuk Institute of Science and Technology, Daegu, Korea 3 TSMC Corporate Research, Hsinchu, Taiwan 4 TSMC Design Technology, Hsinchu, Taiwan *Equally Credited Authors (ECAs) 1 2 Tiny surveillance robots need to efficiently compute a perception front-end workload, consisting of a neural network inference stack, and a localization back-end workload implementing a set of state-space equations. Miniaturization and low-power actuation make bristle robots [1] attractive locomotion platforms, but size limits lead to stringent energy constraints. The edge accelerator needs low leakage for long retentive stretches and efficient matrix compute for active bursts. We present a 0.84TOPS/W, 110μW retentive-sleep-capable resistive random-access memory (RRAM)-based accelerator in
Samuel D. Spetalnick*1, Ashwin Sanjay Lele*1, Brian Crafton1, Muya Chang1,