14 citations · 16 across the 9 of their papers we have counts for
11 papers
Unveiling the Spatial-temporal Effective Receptive Fields of Spiking Neural Networks
Jieyuan Zhang, Xiaolong Zhou, Shuai Wang +6
Spiking Neural Networks (SNNs) demonstrate significant potential for energy-efficient neuromorphic computing through an event-driven paradigm. While training methods and computatio…
SNN: Sub-bit Spiking Neural Networks
Wenjie Wei, Malu Zhang, Jieyuan Zhang +8
Spiking Neural Networks (SNNs) offer an energy-efficient paradigm for machine intelligence, but their continued scaling poses challenges for resource-limited deployment. Despite re…
Dendritic Resonate-and-Fire Neuron for Effective and Efficient Long Sequence Modeling
Dehao Zhang, Malu Zhang, Shuai Wang +6
The explosive growth in sequence length has intensified the demand for effective and efficient long sequence modeling. Benefiting from intrinsic oscillatory membrane dynamics, Reso…
What Is Next for LLMs? Next-Generation AI Computing Hardware Using Photonic Chips
Renjie Li, Wenjie Wei, Qi Xin +7
Large language models (LLMs) are rapidly pushing the limits of contemporary computing hardware. For example, training GPT-3 has been estimated to consume around 1300 MWh of electri…
Quantized Spike-driven Transformer
Xuerui Qiu, Malu Zhang, Jieyuan Zhang +7
Spiking neural networks are emerging as a promising energy-efficient alternative to traditional artificial neural networks due to their spike-driven paradigm. However, recent resea…
Binary Event-Driven Spiking Transformer
Honglin Cao, Zijian Zhou, Wenjie Wei +6
Transformer-based Spiking Neural Networks (SNNs) introduce a novel event-driven self-attention paradigm that combines the high performance of Transformers with the energy efficienc…
Event-Driven Learning for Spiking Neural Networks
Wenjie Wei, Malu Zhang, Jilin Zhang +7
Brain-inspired spiking neural networks (SNNs) have gained prominence in the field of neuromorphic computing owing to their low energy consumption during feedforward inference on ne…
A Comprehensive Analysis of the Effectiveness of Large Language Models as Automatic Dialogue Evaluators
Chen Zhang, Luis Fernando D'Haro, Yiming Chen +2
Automatic evaluation is an integral aspect of dialogue system research. The traditional reference-based NLG metrics are generally found to be unsuitable for dialogue assessment. Co…
LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization
Qianhui Liu, Jiaqi Yan, Malu Zhang +2
Spiking Neural Networks (SNNs) mimic the information-processing mechanisms of the human brain and are highly energy-efficient, making them well-suited for low-power edge devices. H…
LC-TTFS: Towards Lossless Network Conversion for Spiking Neural Networks with TTFS Coding
Qu Yang, Malu Zhang, Jibin Wu +2
The biological neurons use precise spike times, in addition to the spike firing rate, to communicate with each other. The time-to-first-spike (TTFS) coding is inspired by such biol…
xDial-Eval: A Multilingual Open-Domain Dialogue Evaluation Benchmark
Chen Zhang, Luis Fernando D'Haro, Chengguang Tang +3
Recent advancements in reference-free learned metrics for open-domain dialogue evaluation have been driven by the progress in pre-trained language models and the availability of di…