90 citations · 271 across the 49 of their papers we have counts for
13 papers · 1 filter
Memory-Efficient Training with In-Place FFT Implementation
Xinyu Ding, Bangtian Liu, Siyu Liao +1
Fast Fourier Transforms (FFT) are widely used to reduce memory and computational costs in deep learning. However, existing implementations, including standard FFT and real FFT (rFF…
SnipSnap: A Joint Compression Format and Dataflow Co-Optimization Framework for Efficient Sparse LLM Accelerator Design
Junyi Wu, Chao Fang, Zhongfeng Wang
The growing scale of large language models (LLMs) has intensified demands on computation and memory, making efficient inference a key challenge. While sparsity can reduce these cos…
StripDet: Strip Attention-Based Lightweight 3D Object Detection from Point Cloud
Weichao Wang, Wendong Mao, Zhongfeng Wang
The deployment of high-accuracy 3D object detection models from point cloud remains a significant challenge due to their substantial computational and memory requirements. To addre…
APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration
Shaobo Ma, Chao Fang, Haikuo Shao +1
Large language models (LLMs) have revolutionized AI applications, yet their enormous computational demands severely limit deployment and real-time performance. Quantization methods…
A Memory-Efficient Framework for Deformable Transformer with Neural Architecture Search
Wendong Mao, Mingfan Zhao, Jianfeng Guan +2
Deformable Attention Transformers (DAT) have shown remarkable performance in computer vision tasks by adaptively focusing on informative image regions. However, their data-dependen…
Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computing
Qiong Li, Chao Fang, Longwei Huang +2
While posit format offers superior dynamic range and accuracy for transprecision computing, its adoption in RISC-V processors is hindered by the lack of a unified solution for ligh…
FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization
Aotao Wang, Haikuo Shao, Shaobo Ma +1
State Space Models (SSMs), like recent Mamba2, have achieved remarkable performance and received extensive attention. However, deploying Mamba2 on resource-constrained edge devices…
CoDec: Prefix-Shared Decoding Kernel for LLMs
Zhibin Wang, Rui Ning, Chao Fang +12
Prefix-sharing among multiple prompts presents opportunities to combine the operations of the shared prefix, while attention computation in the decode stage, which becomes a critic…
Block Circulant Adapter for Large Language Models
Xinyu Ding, Meiqi Wang, Siyu Liao +1
Fine-tuning large language models (LLMs) is difficult due to their huge model size. Recent Fourier domain-based methods show potential for reducing fine-tuning costs. We propose a…
Parameter-Efficient Fine-Tuning with Circulant and Diagonal Vectors
Xinyu Ding, Lexuan Chen, Siyu Liao +1
Foundation models have achieved tremendous success in different domains. However, their huge computation and storage complexity make these models difficult to fine-tune and also le…
CDM-QTA: Quantized Training Acceleration for Efficient LoRA Fine-Tuning of Diffusion Model
Jinming Lu, Minghao She, Wendong Mao +1
Fine-tuning large diffusion models for custom applications demands substantial power and time, which poses significant challenges for efficient implementation on mobile devices. In…
AccLLM: Accelerating Long-Context LLM Inference Via Algorithm-Hardware Co-Design
Yanbiao Liang, Huihong Shi, Haikuo Shao +1
Recently, large language models (LLMs) have achieved huge success in the natural language processing (NLP) field, driving a growing demand to extend their deployment from the cloud…
An Efficient Sparse Hardware Accelerator for Spike-Driven Transformer
Zhengke Li, Wendong Mao, Siyu Zhang +2
Recently, large models, such as Vision Transformer and BERT, have garnered significant attention due to their exceptional performance. However, their extensive computational requir…