Search Results for author:
Found 9 papers, 6 papers with code
Eagle and Finch: RWKV with Matrix-Valued States and Dynamic Recurrence
We present Eagle (RWKV-5) and Finch (RWKV-6), sequence models improving upon the RWKV (RWKV-4) architecture.
Recent Advances in Scalable Energy-Efficient and Trustworthy Spiking Neural networks: from Algorithms to Technology
Neuromorphic computing and, in particular, spiking neural networks (SNNs) have become an attractive alternative to deep neural networks for a broad range of signal processing applications, processing static and/or temporal inputs from different sensory modalities, including audio and vision sensors.
OR Residual Connection Achieving Comparable Accuracy to ADD Residual Connection in Deep Residual Spiking Neural Networks
Spiking Neural Networks (SNNs) have garnered substantial attention in brain-like computing for their biological fidelity and the capacity to execute energy-efficient spike-driven operations.
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency.
Ranked #4 on
Image Classification
on CIFAR-10
(Accuracy metric)
RWKV: Reinventing RNNs for the Transformer Era
This work presents a significant step towards reconciling trade-offs between computational efficiency and model performance in sequence processing tasks.
Ranked #22 on
Natural Language Inference
on WNLI
Both Efficiency and Effectiveness! A Large Scale Pre-ranking Framework in Search System
In the realm of search systems, multi-stage cascade architecture is a prevalent method, typically consisting of sequential modules such as matching, pre-ranking, and ranking.
SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks
As a result, their performance lags behind modern deep learning, and we are yet to see the effectiveness of SNNs in language generation.
TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks
Given the critical role of attention mechanisms in enhancing neural network performance, the integration of SNNs and attention mechanisms exhibits potential to deliver energy-efficient and high-performance computing paradigms.
SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network
Spiking Neural Networks (SNNs) have piqued researchers' interest because of their capacity to process temporal information and low power consumption.
