90 citations · 271 across the 49 of their papers we have counts for
12 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…
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…
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…
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…
Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores
Shaobo Ma, Chao Fang, Haikuo Shao +1
Large language models (LLMs) have been widely applied but face challenges in efficient inference. While quantization methods reduce computational demands, ultra-low bit quantizatio…
NASH: Neural Architecture and Accelerator Search for Multiplication-Reduced Hybrid Models
Yang Xu, Huihong Shi, Zhongfeng Wang
The significant computational cost of multiplications hinders the deployment of deep neural networks (DNNs) on edge devices. While multiplication-free models offer enhanced hardwar…
Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment
Yuhao Ji, Chao Fang, Shaobo Ma +2
Transformer models have revolutionized AI tasks, but their large size hinders real-world deployment on resource-constrained and latency-critical edge devices. While binarized Trans…
Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design
Chao Fang, Wei Sun, Aojun Zhou +1
Sparse training is one of the promising techniques to reduce the computational cost of DNNs while retaining high accuracy. In particular, N:M fine-grained structured sparsity, wher…
An Efficient FPGA-based Accelerator for Deep Forest
Mingyu Zhu, Jiapeng Luo, Wendong Mao +1
Deep Forest is a prominent machine learning algorithm known for its high accuracy in forecasting. Compared with deep neural networks, Deep Forest has almost no multiplication opera…
GANDSE: Generative Adversarial Network based Design Space Exploration for Neural Network Accelerator Design
Lang Feng, Wenjian Liu, Chuliang Guo +3
With the popularity of deep learning, the hardware implementation platform of deep learning has received increasing interest. Unlike the general purpose devices, e.g., CPU, or GPU,…
Training Deep Neural Networks Using Posit Number System
Jinming Lu, Siyuan Lu, Zhisheng Wang +4
With the increasing size of Deep Neural Network (DNN) models, the high memory space requirements and computational complexity have become an obstacle for efficient DNN implementati…
SGAD: Soft-Guided Adaptively-Dropped Neural Network
Zhisheng Wang, Fangxuan Sun, Jun Lin +2
Deep neural networks (DNNs) have been proven to have many redundancies. Hence, many efforts have been made to compress DNNs. However, the existing model compression methods treat a…