5 papers
DPad: Efficient Diffusion Language Models with Suffix Dropout
Xinhua Chen, Sitao Huang, Cong Guo +5
Diffusion-based Large Language Models (dLLMs) parallelize text generation by framing decoding as a denoising process, but suffer from high computational overhead since they predict…
Phi: Leveraging Pattern-based Hierarchical Sparsity for High-Efficiency Spiking Neural Networks
Chiyue Wei, Bowen Duan, Cong Guo +4
Spiking Neural Networks (SNNs) are gaining attention for their energy efficiency and biological plausibility, utilizing 0-1 activation sparsity through spike-driven computation. Wh…
Ecco: Improving Memory Bandwidth and Capacity for LLMs via Entropy-aware Cache Compression
Feng Cheng, Cong Guo, Chiyue Wei +7
Large language models (LLMs) have demonstrated transformative capabilities across diverse artificial intelligence applications, yet their deployment is hindered by substantial memo…
Transitive Array: An Efficient GEMM Accelerator with Result Reuse
Cong Guo, Chiyue Wei, Jiaming Tang +4
Deep Neural Networks (DNNs) and Large Language Models (LLMs) have revolutionized artificial intelligence, yet their deployment faces significant memory and computational challenges…
Prosperity: Accelerating Spiking Neural Networks via Product Sparsity
Chiyue Wei, Cong Guo, Feng Cheng +4
Spiking Neural Networks (SNNs) are highly efficient due to their spike-based activation, which inherently produces bit-sparse computation patterns. Existing hardware implementation…