Publications by authors named "Zhengqing Zhong"
This paper presents a digital edge neuromorphic spiking neural network (SNN) processor chip for a variety of edge intelligent cognitive applications. This processor allows high-speed, high-accuracy and fully on-chip spike-timing-based multi-layer SNN learning. It is characteristic of hierarchical multi-core architecture, event-driven processing paradigm, meta-crossbar for efficient spike communication, and hybrid and reconfigurable parallelism.
View Article and Find Full Text PDFArticle Synopsis
- * The proposed calcium-gated bipolar leaky integrate and fire (Ca-LIF) neuron model aims to closely replicate the behavior of ReLU neurons common in ANNs, simplifying the conversion process.
- * Their new quantization-aware training (QAT) framework allows for direct export of ANN weights to SNNs without additional processing, resulting in competitive accuracy and shorter inference times across various deep network architectures.