This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient
and robust object detection in resource-constrained platforms. The network architecture is …

With recent advances in learning algorithms, recurrent networks of spiking neurons are
achieving performance that is competitive with vanilla recurrent neural networks. However …

Spiking neural network (SNN) has been attached to great importance due to the properties
of high biological plausibility and low energy consumption on neuromorphic hardware. As …

Introduction The spiking neural network (SNN) is a bionic model that is energy-efficient
when implemented on neuromorphic hardwares. The non-differentiability of the spiking …

The event-driven and sparse nature of communication between spiking neurons in the brain
holds great promise for flexible and energy-efficient AI. Recent advances in learning …

Abstract Spiking Neural Network (SNN), originating from the neural behavior in biology, has
been recognized as one of the next-generation neural networks. Conventionally, SNNs can …

Semantic segmentation predicts dense pixel-wise semantic labels, which is crucial for
autonomous environment perception systems. For applications on mobile devices, current …

We present a processing-in-memory (PIM)-based hardware platform, referred to as
MONETA, for on-chip acceleration of inference and learning in hybrid convolutional spiking …

Human vision incorporates non-uniform resolution retina, efficient eye movement strategy,
and spiking neural network (SNN) to balance the requirements in visual field size, visual …

We present a transfer learning method inspired by modulatory neurotransmitter mechanisms
in biological brains and explore applications for neuromorphic hardware. In this method, the …