AXELRAM: Quantize Once, Never Dequantize
Fuente:
arXiv
Saved in:
| Main Author: | Nishida, Yasushi |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Accelerating PoT Quantization on Edge Devices
by: Saha, Rappy, et al.
Published: (2024)
by: Saha, Rappy, et al.
Published: (2024)
LOTION: Smoothing the Optimization Landscape for Quantized Training
by: Kwun, Mujin, et al.
Published: (2025)
by: Kwun, Mujin, et al.
Published: (2025)
KLLM: Fast LLM Inference with K-Means Quantization
by: Wu, Xueying, et al.
Published: (2025)
by: Wu, Xueying, et al.
Published: (2025)
ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers
by: İslamoğlu, Gamze, et al.
Published: (2023)
by: İslamoğlu, Gamze, et al.
Published: (2023)
PQA: Exploring the Potential of Product Quantization in DNN Hardware Acceleration
by: AbouElhamayed, Ahmed F., et al.
Published: (2023)
by: AbouElhamayed, Ahmed F., et al.
Published: (2023)
Histogram-Equalized Quantization for logic-gated Residual Neural Networks
by: Nguyen, Van Thien, et al.
Published: (2025)
by: Nguyen, Van Thien, et al.
Published: (2025)
EVA: Accelerating LLM Decoding via an Efficient Vector Quantization Architecture
by: Duan, Bowen, et al.
Published: (2026)
by: Duan, Bowen, et al.
Published: (2026)
Fast NF4 Dequantization Kernels for Large Language Model Inference
by: Qi, Xiangbo, et al.
Published: (2026)
by: Qi, Xiangbo, et al.
Published: (2026)
SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference
by: Liu, Qunyou, et al.
Published: (2026)
by: Liu, Qunyou, et al.
Published: (2026)
Exploration of Activation Fault Reliability in Quantized Systolic Array-Based DNN Accelerators
by: Taheri, Mahdi, et al.
Published: (2024)
by: Taheri, Mahdi, et al.
Published: (2024)
FPGA Co-Design for Efficient N:M Sparse and Quantized Model Inference
by: Hsieh, Fen-Yu, et al.
Published: (2025)
by: Hsieh, Fen-Yu, et al.
Published: (2025)
Exploring Quantization and Mapping Synergy in Hardware-Aware Deep Neural Network Accelerators
by: Klhufek, Jan, et al.
Published: (2024)
by: Klhufek, Jan, et al.
Published: (2024)
Binary Weight Multi-Bit Activation Quantization for Compute-in-Memory CNN Accelerators
by: Zhou, Wenyong, et al.
Published: (2025)
by: Zhou, Wenyong, et al.
Published: (2025)
MCEL: Margin-Based Cross-Entropy Loss for Error-Tolerant Quantized Neural Networks
by: Yayla, Mikail, et al.
Published: (2026)
by: Yayla, Mikail, et al.
Published: (2026)
MixDiT: Accelerating Image Diffusion Transformer Inference with Mixed-Precision MX Quantization
by: Kim, Daeun, et al.
Published: (2025)
by: Kim, Daeun, et al.
Published: (2025)
Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization
by: Kim, Minsu, et al.
Published: (2025)
by: Kim, Minsu, et al.
Published: (2025)
A Hardware-Aware, Per-Layer Methodology for Post-Training Quantization of Large Language Models
by: Killian, Earl
Published: (2026)
by: Killian, Earl
Published: (2026)
FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference
by: Hooper, Coleman, et al.
Published: (2025)
by: Hooper, Coleman, et al.
Published: (2025)
FineQ: Software-Hardware Co-Design for Low-Bit Fine-Grained Mixed-Precision Quantization of LLMs
by: Xie, Xilong, et al.
Published: (2025)
by: Xie, Xilong, et al.
Published: (2025)
SeVeDo: A Heterogeneous Transformer Accelerator for Low-Bit Inference via Hierarchical Group Quantization and SVD-Guided Mixed Precision
by: Choi, Yuseon, et al.
Published: (2025)
by: Choi, Yuseon, et al.
Published: (2025)
A2Q+: Improving Accumulator-Aware Weight Quantization
by: Colbert, Ian, et al.
Published: (2024)
by: Colbert, Ian, et al.
Published: (2024)
PoTAcc: A Pipeline for End-to-End Acceleration of Power-of-Two Quantized DNNs
by: Saha, Rappy, et al.
Published: (2026)
by: Saha, Rappy, et al.
Published: (2026)
SONIQ: System-Optimized Noise-Injected Ultra-Low-Precision Quantization with Full-Precision Parity
by: Zhou, Cyrus, et al.
Published: (2023)
by: Zhou, Cyrus, et al.
Published: (2023)
Scaling Laws for Floating Point Quantization Training
by: Sun, Xingwu, et al.
Published: (2025)
by: Sun, Xingwu, et al.
Published: (2025)
Improving Quantization with Post-Training Model Expansion
by: Franco, Giuseppe, et al.
Published: (2025)
by: Franco, Giuseppe, et al.
Published: (2025)
Tensor-Compressed and Fully-Quantized Training of Neural PDE Solvers
by: Lu, Jinming, et al.
Published: (2025)
by: Lu, Jinming, et al.
Published: (2025)
On-Chip Hardware-Aware Quantization for Mixed Precision Neural Networks
by: Huang, Wei, et al.
Published: (2023)
by: Huang, Wei, et al.
Published: (2023)
Unveiling the Potential of Quantization with MXFP4: Strategies for Quantization Error Reduction
by: Chhugani, Jatin, et al.
Published: (2026)
by: Chhugani, Jatin, et al.
Published: (2026)
FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression
by: Qiao, Ye, et al.
Published: (2026)
by: Qiao, Ye, et al.
Published: (2026)
AQPIM: Breaking the PIM Capacity Wall for LLMs with In-Memory Activation Quantization
by: Matsushima, Kosuke, et al.
Published: (2026)
by: Matsushima, Kosuke, et al.
Published: (2026)
MixPE: Quantization and Hardware Co-design for Efficient LLM Inference
by: Zhang, Yu, et al.
Published: (2024)
by: Zhang, Yu, et al.
Published: (2024)
Neural Network Quantization for Microcontrollers: A Comprehensive Survey of Methods, Platforms, and Applications
by: Abushahla, Hamza A., et al.
Published: (2025)
by: Abushahla, Hamza A., et al.
Published: (2025)
OPAL: Outlier-Preserved Microscaling Quantization Accelerator for Generative Large Language Models
by: Koo, Jahyun, et al.
Published: (2024)
by: Koo, Jahyun, et al.
Published: (2024)
RESQ: A Unified Framework for REliability- and Security Enhancement of Quantized Deep Neural Networks
by: Mohammadi, Ali Soltan, et al.
Published: (2026)
by: Mohammadi, Ali Soltan, et al.
Published: (2026)
MicroScopiQ: Accelerating Foundational Models through Outlier-Aware Microscaling Quantization
by: Ramachandran, Akshat, et al.
Published: (2024)
by: Ramachandran, Akshat, et al.
Published: (2024)
Column-wise Quantization of Weights and Partial Sums for Accurate and Efficient Compute-In-Memory Accelerators
by: Kim, Jiyoon, et al.
Published: (2025)
by: Kim, Jiyoon, et al.
Published: (2025)
Late Breaking Results: Quamba-SE: Soft-edge Quantizer for Activations in State Space Models
by: Chen, Yizhi, et al.
Published: (2026)
by: Chen, Yizhi, et al.
Published: (2026)
SmartQuant: CXL-based AI Model Store in Support of Runtime Configurable Weight Quantization
by: Xie, Rui, et al.
Published: (2024)
by: Xie, Rui, et al.
Published: (2024)
FaTRQ: Tiered Residual Quantization for LLM Vector Search in Far-Memory-Aware ANNS Systems
by: Zhang, Tianqi, et al.
Published: (2026)
by: Zhang, Tianqi, et al.
Published: (2026)
Genetic Quantization-Aware Approximation for Non-Linear Operations in Transformers
by: Dong, Pingcheng, et al.
Published: (2024)
by: Dong, Pingcheng, et al.
Published: (2024)
Similar Items
-
Accelerating PoT Quantization on Edge Devices
by: Saha, Rappy, et al.
Published: (2024) -
LOTION: Smoothing the Optimization Landscape for Quantized Training
by: Kwun, Mujin, et al.
Published: (2025) -
KLLM: Fast LLM Inference with K-Means Quantization
by: Wu, Xueying, et al.
Published: (2025) -
ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers
by: İslamoğlu, Gamze, et al.
Published: (2023) -
PQA: Exploring the Potential of Product Quantization in DNN Hardware Acceleration
by: AbouElhamayed, Ahmed F., et al.
Published: (2023)