G. C. Qiao

TL;DR: This article introduces a vehicular edge multi-access network that treats vehicles as edge computation resources to construct the cooperative and distributed computing architecture and proposes a collaborative task offloading and output transmission mechanism to guarantee low latency as well as the application- level performance.

TL;DR: This paper proposes a low-complex algorithm that decouples the joint optimization into the location updating approach and the association matching approach and shows that the proposed schemes can efficiently solve the target problems while striking a better overall system utility, compared with other traditional deployment and management strategies.

TL;DR: A bio-plausible online-learning spiking neural network (SNN) model for hardware implementation that reduces the hardware resources and power consumption by 40.7% and 36.3%, respectively (under 55-nm CMOS process).

TL;DR: The HP Memristor is introduced to a TCNN to develop a memristor-based transient chaotic neural network (MTCNN) model that is highly efficient, converges quickly, and has significant prospects for physical implementation.

TL;DR: A compact, programmable, versatile, and scalable neuromorphic architecture that effectively alleviates the hardware explosion in fully-connected architecture and effectively realizes different convolution operations which are basic computing operations in convolutional neural networks.