41 citations · 93 across the 15 of their papers we have counts for
15 papers
FastCar: Cache Attentive Replay for Fast Auto-Regressive Video Generation on the Edge
Xuan Shen, Weize Ma, Yufa Zhou +11
Auto-regressive (AR) models, initially successful in language generation, have recently shown promise in visual generation tasks due to their superior sampling efficiency. Unlike i…
LUTMUL: Exceed Conventional FPGA Roofline Limit by LUT-based Efficient Multiplication for Neural Network Inference
Yanyue Xie, Zhengang Li, Dana Diaconu +3
For FPGA-based neural network accelerators, digital signal processing (DSP) blocks have traditionally been the cornerstone for handling multiplications. This paper introduces LUTMU…
SuperFlow: A Fully-Customized RTL-to-GDS Design Automation Flow for Adiabatic Quantum-Flux-Parametron Superconducting Circuits
Yanyue Xie, Peiyan Dong, Geng Yuan +10
Superconducting circuits, like Adiabatic Quantum-Flux-Parametron (AQFP), offer exceptional energy efficiency but face challenges in physical design due to sophisticated spacing and…
Quasar-ViT: Hardware-Oriented Quantization-Aware Architecture Search for Vision Transformers
Zhengang Li, Alec Lu, Yanyue Xie +9
Vision transformers (ViTs) have demonstrated their superior accuracy for computer vision tasks compared to convolutional neural networks (CNNs). However, ViT models are often compu…
SNED: Superposition Network Architecture Search for Efficient Video Diffusion Model
Zhengang Li, Yan Kang, Yuchen Liu +4
While AI-generated content has garnered significant attention, achieving photo-realistic video synthesis remains a formidable challenge. Despite the promising advances in diffusion…
A Video Coding Method Based on Neural Network for CLIC2024
Zhengang Li, Jingchi Zhang, Yonghua Wang +5
This paper presents a video coding scheme that combines traditional optimization methods with deep learning methods based on the Enhanced Compression Model (ECM). In this paper, th…
Agile-Quant: Activation-Guided Quantization for Faster Inference of LLMs on the Edge
Xuan Shen, Peiyan Dong, Lei Lu +5
Large Language Models (LLMs) stand out for their impressive performance in intricate language modeling tasks. However, their demanding computational and memory needs pose obstacles…
Outlier Suppression+: Accurate quantization of large language models by equivalent and optimal shifting and scaling
Xiuying Wei, Yunchen Zhang, Yuhang Li +4
Post-training quantization~(PTQ) of transformer language models faces significant challenges due to the existence of detrimental outliers in activations. We observe that these outl…
F8Net: Fixed-Point 8-bit Only Multiplication for Network Quantization
Qing Jin, Jian Ren, Richard Zhuang +6
Neural network quantization is a promising compression technique to reduce memory footprint and save energy consumption, potentially leading to real-time inference. However, there…
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration
Yifan Gong, Geng Yuan, Zheng Zhan +9
Weight pruning is an effective model compression technique to tackle the challenges of achieving real-time deep neural network (DNN) inference on mobile devices. However, prior pru…
MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge
Geng Yuan, Xiaolong Ma, Wei Niu +13
Recently, a new trend of exploring sparsity for accelerating neural network training has emerged, embracing the paradigm of training on the edge. This paper proposes a novel Memory…
Improving DNN Fault Tolerance using Weight Pruning and Differential Crossbar Mapping for ReRAM-based Edge AI
Geng Yuan, Zhiheng Liao, Xiaolong Ma +11
Recent research demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vecto…
FORMS: Fine-grained Polarized ReRAM-based In-situ Computation for Mixed-signal DNN Accelerator
Geng Yuan, Payman Behnam, Zhengang Li +8
Recent works demonstrated the promise of using resistive random access memory (ReRAM) as an emerging technology to perform inherently parallel analog domain in-situ matrix-vector m…
Towards Fast and Accurate Multi-Person Pose Estimation on Mobile Devices
Xuan Shen, Geng Yuan, Wei Niu +5
The rapid development of autonomous driving, abnormal behavior detection, and behavior recognition makes an increasing demand for multi-person pose estimation-based applications, e…
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration
Zhengang Li, Geng Yuan, Wei Niu +13
With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Pri…