3 citations · 7 across the 7 of their papers we have counts for
7 papers
SATA: Sparsity-Aware Scheduling for Selective Token Attention
Zhenkun Fan, Zishen Wan, Che-Kai Liu +5
Transformers have become the foundation of numerous state-of-the-art AI models across diverse domains, thanks to their powerful attention mechanism for modeling long-range dependen…
FAVbot: An Autonomous Target Tracking Micro-Robot with Frequency Actuation Control
Zhijian Hao, Ashwin Lele, Yan Fang +2
Robotic autonomy at centimeter scale requires compact and miniaturization-friendly actuation integrated with sensing and neural network processing assembly within a tiny form facto…
Fusing Frame and Event Vision for High-speed Optical Flow for Edge Application
Ashwin Sanjay Lele, Arijit Raychowdhury
Optical flow computation with frame-based cameras provides high accuracy but the speed is limited either by the model size of the algorithm or by the frame rate of the camera. This…
Robotic Computing on FPGAs: Current Progress, Research Challenges, and Opportunities
Zishen Wan, Ashwin Lele, Bo Yu +5
Robotic computing has reached a tipping point, with a myriad of robots (e.g., drones, self-driving cars, logistic robots) being widely applied in diverse scenarios. The continuous…
Circuit and System Technologies for Energy-Efficient Edge Robotics
Zishen Wan, Ashwin Sanjay Lele, Arijit Raychowdhury
As we march towards the age of ubiquitous intelligence, we note that AI and intelligence are progressively moving from the cloud to the edge. The success of Edge-AI is pivoted on i…
Bio-inspired Gait Imitation of Hexapod Robot Using Event-Based Vision Sensor and Spiking Neural Network
Justin Ting, Yan Fang, Ashwin Sanjay Lele +1
Learning how to walk is a sophisticated neurological task for most animals. In order to walk, the brain must synthesize multiple cortices, neural circuits, and diverse sensory inpu…
Learning to Walk: Spike Based Reinforcement Learning for Hexapod Robot Central Pattern Generation
Ashwin Sanjay Lele, Yan Fang, Justin Ting +1
Learning to walk -- i.e., learning locomotion under performance and energy constraints continues to be a challenge in legged robotics. Methods such as stochastic gradient, deep rei…