Cover Image

Co-Designing Binarized Transformer and Hardware Accelerator for Efficient End-to-End Edge Deployment

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ji, Yuhao, Fang, Chao, Ma, Shaobo, Shao, Haikuo, Wang, Zhongfeng
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Efficient Arbitrary Precision Acceleration for Large Language Models on GPU Tensor Cores
by: Ma, Shaobo, et al.
Published: (2024)

APT-LLM: Exploiting Arbitrary-Precision Tensor Core Computing for LLM Acceleration
by: Ma, Shaobo, et al.
Published: (2025)

BETA: Binarized Energy-Efficient Transformer Accelerator at the Edge
by: Ji, Yuhao, et al.
Published: (2024)

AccLLM: Accelerating Long-Context LLM Inference Via Algorithm-Hardware Co-Design
by: Liang, Yanbiao, et al.
Published: (2025)

FastMamba: A High-Speed and Efficient Mamba Accelerator on FPGA with Accurate Quantization
by: Wang, Aotao, et al.
Published: (2025)

An FPGA-Based Reconfigurable Accelerator for Convolution-Transformer Hybrid EfficientViT
by: Shao, Haikuo, et al.
Published: (2024)

Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision Transformer
by: Shi, Huihong, et al.
Published: (2024)

FinWorld: An All-in-One Open-Source Platform for End-to-End Financial AI Research and Deployment
by: Zhang, Wentao, et al.
Published: (2025)

Attack End-to-End Autonomous Driving through Module-Wise Noise
by: Wang, Lu, et al.
Published: (2024)

AnyBipe: An End-to-End Framework for Training and Deploying Bipedal Robots Guided by Large Language Models
by: Yao, Yifei, et al.
Published: (2024)

NASH: Neural Architecture and Accelerator Search for Multiplication-Reduced Hybrid Models
by: Xu, Yang, et al.
Published: (2024)

PlatformX: An End-to-End Transferable Platform for Energy-Efficient Neural Architecture Search
by: Tu, Xiaolong, et al.
Published: (2025)

An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems
by: Miao, Changhao, et al.
Published: (2025)

Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems
by: Zhou, Ao, et al.
Published: (2024)

GinAR: An End-To-End Multivariate Time Series Forecasting Model Suitable for Variable Missing
by: Yu, Chengqing, et al.
Published: (2024)

ExACT: An End-to-End Autonomous Excavator System Using Action Chunking With Transformers
by: Chen, Liangliang, et al.
Published: (2024)

L-MoE: End-to-End Training of a Lightweight Mixture of Low-Rank Adaptation Experts
by: Ji, Shihao, et al.
Published: (2025)

Towards Resource-Efficient LLMs: End-to-End Energy Accounting of Distillation Pipelines
by: Lambert, Katherine, et al.
Published: (2026)

From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks
by: Geng, Xue, et al.
Published: (2024)

An End-to-End Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drones
by: Zeng, Taihelong, et al.
Published: (2025)

EvaDrive: Evolutionary Adversarial Policy Optimization for End-to-End Autonomous Driving
by: Jiao, Siwen, et al.
Published: (2025)

BPQP: A Differentiable Convex Optimization Framework for Efficient End-to-End Learning
by: Pan, Jianming, et al.
Published: (2024)

Anda: Unlocking Efficient LLM Inference with a Variable-Length Grouped Activation Data Format
by: Fang, Chao, et al.
Published: (2024)

MC$^2$A: Enabling Algorithm-Hardware Co-Design for Efficient Markov Chain Monte Carlo Acceleration
by: Zhao, Shirui, et al.
Published: (2025)

Bridging the Divide: End-to-End Sequence-Graph Learning
by: Chen, Yuen, et al.
Published: (2025)

ExChanGeAI: An End-to-End Platform and Efficient Foundation Model for Electrocardiogram Analysis and Fine-tuning
by: Bickmann, Lucas, et al.
Published: (2025)

Stratos: An End-to-End Distillation Pipeline for Customized LLMs under Distributed Cloud Environments
by: Dai, Ziming, et al.
Published: (2025)

Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
by: Zheng, Yinan, et al.
Published: (2026)

Hamming Attention Distillation: Binarizing Keys and Queries for Efficient Long-Context Transformers
by: Horton, Mark, et al.
Published: (2025)

DISCO: An End-to-End Bandit Framework for Personalised Discount Allocation
by: Zhang, Jason Shuo, et al.
Published: (2024)

End-To-End Learning of Gaussian Mixture Priors for Diffusion Sampler
by: Blessing, Denis, et al.
Published: (2025)

FT-Transformer: Resilient and Reliable Transformer with End-to-End Fault Tolerant Attention
by: Dai, Huangliang, et al.
Published: (2025)

Self-Evolving Recommendation System: End-To-End Autonomous Model Optimization With LLM Agents
by: Wang, Haochen, et al.
Published: (2026)

ElasticAI: Creating and Deploying Energy-Efficient Deep Learning Accelerator for Pervasive Computing
by: Qian, Chao, et al.
Published: (2024)

Resource-Efficient Generative AI Model Deployment in Mobile Edge Networks
by: Liang, Yuxin, et al.
Published: (2024)

Identifying Functionally Important Features with End-to-End Sparse Dictionary Learning
by: Braun, Dan, et al.
Published: (2024)

End-to-End Learning for Partially-Observed Time Series with PyPOTS
by: Du, Wenjie, et al.
Published: (2026)

Graph-based Time Series Clustering for End-to-End Hierarchical Forecasting
by: Cini, Andrea, et al.
Published: (2023)

LEAP: Learnable End-to-End Adaptive Pruning of Large Language Models
by: Mozaffari, Mohammad, et al.
Published: (2026)

End-to-End On-Device Quantization-Aware Training for LLMs at Inference Cost
by: Tan, Qitao, et al.
Published: (2025)