Copertina

ShiftAddNet: A Hardware-Inspired Deep Network

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	You, Haoran, Chen, Xiaohan, Zhang, Yongan, Li, Chaojian, Li, Sicheng, Liu, Zihao, Wang, Zhangyang, Lin, Yingyan Celine
Natura:	Preprint
Pubblicazione:	2020
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

ShiftAddNAS: Hardware-Inspired Search for More Accurate and Efficient Neural Networks
di: You, Haoran, et al.
Pubblicazione: (2022)

ShiftAddViT: Mixture of Multiplication Primitives Towards Efficient Vision Transformer
di: You, Haoran, et al.
Pubblicazione: (2023)

A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning
di: Fu, Yonggan, et al.
Pubblicazione: (2021)

Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks
di: You, Haoran, et al.
Pubblicazione: (2019)

InstantNet: Automated Generation and Deployment of Instantaneously Switchable-Precision Networks
di: Fu, Yonggan, et al.
Pubblicazione: (2021)

HW-NAS-Bench:Hardware-Aware Neural Architecture Search Benchmark
di: Li, Chaojian, et al.
Pubblicazione: (2021)

DNA: Differentiable Network-Accelerator Co-Search
di: Zhang, Yongan, et al.
Pubblicazione: (2020)

GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design
di: You, Haoran, et al.
Pubblicazione: (2021)

ViTCoD: Vision Transformer Acceleration via Dedicated Algorithm and Accelerator Co-Design
di: You, Haoran, et al.
Pubblicazione: (2022)

DANCE: DAta-Network Co-optimization for Efficient Segmentation Model Training and Inference
di: Li, Chaojian, et al.
Pubblicazione: (2021)

ShiftAddLLM: Accelerating Pretrained LLMs via Post-Training Multiplication-Less Reparameterization
di: You, Haoran, et al.
Pubblicazione: (2024)

Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search
di: Fu, Yonggan, et al.
Pubblicazione: (2020)

GPT4AIGChip: Towards Next-Generation AI Accelerator Design Automation via Large Language Models
di: Fu, Yonggan, et al.
Pubblicazione: (2023)

AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
di: Fu, Yonggan, et al.
Pubblicazione: (2020)

Auto-NBA: Efficient and Effective Search Over the Joint Space of Networks, Bitwidths, and Accelerators
di: Fu, Yonggan, et al.
Pubblicazione: (2021)

ERSAM: Neural Architecture Search For Energy-Efficient and Real-Time Social Ambiance Measurement
di: Li, Chaojian, et al.
Pubblicazione: (2023)

2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency
di: Fu, Yonggan, et al.
Pubblicazione: (2021)

FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
di: Fu, Yonggan, et al.
Pubblicazione: (2020)

MG-Verilog: Multi-grained Dataset Towards Enhanced LLM-assisted Verilog Generation
di: Zhang, Yongan, et al.
Pubblicazione: (2024)

SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning
di: You, Haoran, et al.
Pubblicazione: (2022)

ShiftAddAug: Augment Multiplication-Free Tiny Neural Network with Hybrid Computation
di: Guo, Yipin, et al.
Pubblicazione: (2024)

Max-Affine Spline Insights Into Deep Network Pruning
di: You, Haoran, et al.
Pubblicazione: (2021)

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
di: Li, Chaojian, et al.
Pubblicazione: (2025)

Early-Bird GCNs: Graph-Network Co-Optimization Towards More Efficient GCN Training and Inference via Drawing Early-Bird Lottery Tickets
di: You, Haoran, et al.
Pubblicazione: (2021)

From Inference Efficiency to Embodied Efficiency: Revisiting Efficiency Metrics for Vision-Language-Action Models
di: Li, Zhuofan, et al.
Pubblicazione: (2026)

When Linear Attention Meets Autoregressive Decoding: Towards More Effective and Efficient Linearized Large Language Models
di: You, Haoran, et al.
Pubblicazione: (2024)

MixRT: Mixed Neural Representations For Real-Time NeRF Rendering
di: Li, Chaojian, et al.
Pubblicazione: (2023)

Early-Bird Diffusion: Investigating and Leveraging Timestep-Aware Early-Bird Tickets in Diffusion Models for Efficient Training
di: Whalen, Lexington, et al.
Pubblicazione: (2025)

Uni-Render: A Unified Accelerator for Real-Time Rendering Across Diverse Neural Renderers
di: Li, Chaojian, et al.
Pubblicazione: (2025)

CPT: Efficient Deep Neural Network Training via Cyclic Precision
di: Fu, Yonggan, et al.
Pubblicazione: (2021)

i-FlatCam: A 253 FPS, 91.49 $μ$J/Frame Ultra-Compact Intelligent Lensless Camera for Real-Time and Efficient Eye Tracking in VR/AR
di: Zhao, Yang, et al.
Pubblicazione: (2022)

RT-NeRF: Real-Time On-Device Neural Radiance Fields Towards Immersive AR/VR Rendering
di: Li, Chaojian, et al.
Pubblicazione: (2022)

DepthShrinker: A New Compression Paradigm Towards Boosting Real-Hardware Efficiency of Compact Neural Networks
di: Fu, Yonggan, et al.
Pubblicazione: (2022)

Gen-NeRF: Efficient and Generalizable Neural Radiance Fields via Algorithm-Hardware Co-Design
di: Fu, Yonggan, et al.
Pubblicazione: (2023)

AmoebaLLM: Constructing Any-Shape Large Language Models for Efficient and Instant Deployment
di: Fu, Yonggan, et al.
Pubblicazione: (2024)

Design and Implementation of an FPGA-Based Hardware Accelerator for Transformer
di: Li, Richie, et al.
Pubblicazione: (2025)

A&B BNN: Add&Bit-Operation-Only Hardware-Friendly Binary Neural Network
di: Ma, Ruichen, et al.
Pubblicazione: (2024)

Instant-3D: Instant Neural Radiance Field Training Towards On-Device AR/VR 3D Reconstruction
di: Li, Sixu, et al.
Pubblicazione: (2023)

Gaussian Blending Unit: An Edge GPU Plug-in for Real-Time Gaussian-Based Rendering in AR/VR
di: Ye, Zhifan, et al.
Pubblicazione: (2025)

EyeCoD: Eye Tracking System Acceleration via FlatCam-based Algorithm & Accelerator Co-Design
di: You, Haoran, et al.
Pubblicazione: (2022)