Copertina

True 4-Bit Quantized Convolutional Neural Network Training on CPU: Achieving Full-Precision Parity

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autore principale:	Tathe, Shivnath
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

LACE: Loss-Adaptive Capacity Expansion for Continual Learning
di: Tathe, Shivnath
Pubblicazione: (2026)

DyBit: Dynamic Bit-Precision Numbers for Efficient Quantized Neural Network Inference
di: Zhou, Jiajun, et al.
Pubblicazione: (2023)

SONIQ: System-Optimized Noise-Injected Ultra-Low-Precision Quantization with Full-Precision Parity
di: Zhou, Cyrus, et al.
Pubblicazione: (2023)

Improving Quantization-aware Training of Low-Precision Network via Block Replacement on Full-Precision Counterpart
di: Yu, Chengting, et al.
Pubblicazione: (2024)

Attn-QAT: 4-Bit Attention With Quantization-Aware Training
di: Zhang, Peiyuan, et al.
Pubblicazione: (2026)

ECO: Quantized Training without Full-Precision Master Weights
di: Nikdan, Mahdi, et al.
Pubblicazione: (2026)

BitSnap: Checkpoint Sparsification and Quantization in LLM Training
di: Peng, Yanxin, et al.
Pubblicazione: (2025)

Quantization Variation: A New Perspective on Training Transformers with Low-Bit Precision
di: Huang, Xijie, et al.
Pubblicazione: (2023)

Efficient Mixed Precision Quantization in Graph Neural Networks
di: Moustafa, Samir, et al.
Pubblicazione: (2025)

SplitQuant: Layer Splitting for Low-Bit Neural Network Quantization
di: Song, Jaewoo, et al.
Pubblicazione: (2025)

Multiscale Training of Convolutional Neural Networks
di: Ahamed, Shadab, et al.
Pubblicazione: (2025)

Where and How to Enhance: Discovering Bit-Width Contribution for Mixed Precision Quantization
di: Kang, Haidong, et al.
Pubblicazione: (2025)

LoRAQuant: Mixed-Precision Quantization of LoRA to Ultra-Low Bits
di: Mirzaei, Amir Reza, et al.
Pubblicazione: (2025)

TruncQuant: Truncation-Ready Quantization for DNNs with Flexible Weight Bit Precision
di: Kim, Jinhee, et al.
Pubblicazione: (2025)

Verification of Bit-Flip Attacks against Quantized Neural Networks
di: Zhang, Yedi, et al.
Pubblicazione: (2025)

Quantized Convolutional Neural Networks Through the Lens of Partial Differential Equations
di: Ben-Yair, Ido, et al.
Pubblicazione: (2021)

CLAQ: Pushing the Limits of Low-Bit Post-Training Quantization for LLMs
di: Wang, Haoyu, et al.
Pubblicazione: (2024)

AdaQAT: Adaptive Bit-Width Quantization-Aware Training
di: Gernigon, Cédric, et al.
Pubblicazione: (2024)

Gradient-Free Training of Quantized Neural Networks
di: Cohen, Noa, et al.
Pubblicazione: (2024)

Hardness of Learning Fixed Parities with Neural Networks
di: Shoshani, Itamar, et al.
Pubblicazione: (2025)

Every Bit Counts: A Theoretical Study of Precision-Expressivity Tradeoffs in Quantized Transformers
di: Chakrabarti, Sayak, et al.
Pubblicazione: (2026)

Bits for Privacy: Evaluating Post-Training Quantization via Membership Inference
di: Zhang, Chenxiang, et al.
Pubblicazione: (2025)

Outlier-Safe Pre-Training for Robust 4-Bit Quantization of Large Language Models
di: Park, Jungwoo, et al.
Pubblicazione: (2025)

HBVLA: Pushing 1-Bit Post-Training Quantization for Vision-Language-Action Models
di: Yan, Xin, et al.
Pubblicazione: (2026)

TesseraQ: Ultra Low-Bit LLM Post-Training Quantization with Block Reconstruction
di: Li, Yuhang, et al.
Pubblicazione: (2024)

Low-Bit Quantization Favors Undertrained LLMs: Scaling Laws for Quantized LLMs with 100T Training Tokens
di: Ouyang, Xu, et al.
Pubblicazione: (2024)

Outlier-Aware Training for Low-Bit Quantization of Structural Re-Parameterized Networks
di: Niu, Muqun, et al.
Pubblicazione: (2024)

NoProp: Training Neural Networks without Full Back-propagation or Full Forward-propagation
di: Li, Qinyu, et al.
Pubblicazione: (2025)

FAMES: Fast Approximate Multiplier Substitution for Mixed-Precision Quantized DNNs--Down to 2 Bits!
di: Ren, Yi, et al.
Pubblicazione: (2024)

SDP4Bit: Toward 4-bit Communication Quantization in Sharded Data Parallelism for LLM Training
di: Jia, Jinda, et al.
Pubblicazione: (2024)

Joint Pruning and Channel-wise Mixed-Precision Quantization for Efficient Deep Neural Networks
di: Motetti, Beatrice Alessandra, et al.
Pubblicazione: (2024)

Robust Ultra Low-Bit Post-Training Quantization via Stable Diagonal Curvature Estimate
di: Kim, Jaemin, et al.
Pubblicazione: (2026)

MSQ: Memory-Efficient Bit Sparsification Quantization
di: Han, Seokho, et al.
Pubblicazione: (2025)

One-Bit Quantization for Random Features Models
di: Akhtiamov, Danil, et al.
Pubblicazione: (2025)

On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks
di: Huang, Wei, et al.
Pubblicazione: (2023)

Understanding the Difficulty of Low-Precision Post-Training Quantization for LLMs
di: Xu, Zifei, et al.
Pubblicazione: (2024)

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM
di: Wang, Dongwei, et al.
Pubblicazione: (2026)

True Online TD-Replan(lambda) Achieving Planning through Replaying
di: Altahhan, Abdulrahman
Pubblicazione: (2025)

Convolutional Neural Network Achieves Human-level Accuracy in Music Genre Classification
di: Dong, Mingwen
Pubblicazione: (2018)

BitLogic: Training Framework for Gradient-Based FPGA-Native Neural Networks
di: Bührer, Simon, et al.
Pubblicazione: (2026)