1 citations · 2 across the 8 of their papers we have counts for
10 papers
ForesightSafety Bench: A Frontier Risk Evaluation and Governance Framework towards Safe AI
Haibo Tong, Feifei Zhao, Linghao Feng +18
Rapidly evolving AI exhibits increasingly strong autonomy and goal-directed capabilities, accompanied by derivative systemic risks that are more unpredictable, difficult to control…
FireFly-P: FPGA-Accelerated Spiking Neural Network Plasticity for Robust Adaptive Control
Tenglong Li, Jindong Li, Guobin Shen +3
Spiking Neural Networks (SNNs) offer a biologically plausible learning mechanism through synaptic plasticity, enabling unsupervised adaptation without the computational overhead of…
Hummingbird: A Smaller and Faster Large Language Model Accelerator on Embedded FPGA
Jindong Li, Tenglong Li, Ruiqi Chen +4
Deploying large language models (LLMs) on embedded devices remains a significant research challenge due to the high computational and memory demands of LLMs and the limited hardwar…
FireFly-T: High-Throughput Sparsity Exploitation for Spiking Transformer Acceleration with Dual-Engine Overlay Architecture
Tenglong Li, Jindong Li, Guobin Shen +3
Spiking transformers are emerging as a promising architecture that combines the energy efficiency of Spiking Neural Networks (SNNs) with the powerful attention mechanisms of transf…
STEP: A Unified Spiking Transformer Evaluation Platform for Fair and Reproducible Benchmarking
Sicheng Shen, Dongcheng Zhao, Linghao Feng +5
Spiking Transformers have recently emerged as promising architectures for combining the efficiency of spiking neural networks with the representational power of self-attention. How…
Pushing up to the Limit of Memory Bandwidth and Capacity Utilization for Efficient LLM Decoding on Embedded FPGA
Jindong Li, Tenglong Li, Guobin Shen +3
The extremely high computational and storage demands of large language models have excluded most edge devices, which were widely used for efficient machine learning, from being via…
: Enhanced Information Flow in Spiking Neural Networks with High Hardware Compatibility
Guobin Shen, Jindong Li, Tenglong Li +2
Spiking Neural Networks (SNNs) hold promise for energy-efficient, biologically inspired computing. We identify substantial informatio loss during spike transmission, linked to temp…
Revealing Untapped DSP Optimization Potentials for FPGA-Based Systolic Matrix Engines
Jindong Li, Tenglong Li, Guobin Shen +3
Systolic architectures are widely embraced by neural network accelerators for their superior performance in highly parallelized computation. The DSP48E2s serve as dedicated arithme…
FireFly-S: Exploiting Dual-Side Sparsity for Spiking Neural Networks Acceleration with Reconfigurable Spatial Architecture
Tenglong Li, Jindong Li, Guobin Shen +3
Spiking Neural Networks (SNNs), with brain-inspired structure using discrete spikes instead of continuous activations, are gaining attention for their efficient processing on neuro…
Is Conventional SNN Really Efficient? A Perspective from Network Quantization
Guobin Shen, Dongcheng Zhao, Tenglong Li +2
Spiking Neural Networks (SNNs) have been widely praised for their high energy efficiency and immense potential. However, comprehensive research that critically contrasts and correl…