Kai Huang

TL;DR: The basic principles of the DOL, the specification mechanisms for applications, platform and mapping as well as its internal analytic performance evaluation framework are presented and an MPEG -2 decoder case study is presented.

TL;DR: This paper surveys the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications, and highlights the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities.

TL;DR: A novel technique are proposed to directly model the idle intervals of individual cores such that both DVFS and DPM can be optimized at the same time.

TL;DR: Experimental results demonstrated that the proposed robot system for subretinal insertion integrated with intraoperative optical coherence tomography (OCT) has the ability to improve surgical outcomes by surgeons overcoming their physical limitations in order to enable a better dexterous motion, and furthermore enhancing their visual feedback for a better intraocular perception.

TL;DR: This paper introduces an end to end learning approach of spiking neural networks for a lane keeping vehicle that considers the reward-modulated spike-timing-dependent-plasticity (R-STDP) as a promising solution in training SNNs, since it combines the advantages of both reinforcement learning and the well-known STDP.