Leveraging Stochastic Depth Training for Adaptive Inference
Fuente:
arXiv
Guardado en:
| Autores principales: | Korol, Guilherme, Beck, Antonio Carlos Schneider, Castrillon, Jeronimo |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Full-Stack Optimization for CAM-Only DNN Inference
por: de Lima, João Paulo C., et al.
Publicado: (2024)
por: de Lima, João Paulo C., et al.
Publicado: (2024)
Efficient In-Memory Acceleration of Sparse Block Diagonal LLMs
por: de Lima, João Paulo Cardoso, et al.
Publicado: (2025)
por: de Lima, João Paulo Cardoso, et al.
Publicado: (2025)
Leveraging Highly Approximated Multipliers in DNN Inference
por: Zervakis, Georgios, et al.
Publicado: (2024)
por: Zervakis, Georgios, et al.
Publicado: (2024)
Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations
por: Esmaeilzadeh, Hadi, et al.
Publicado: (2023)
por: Esmaeilzadeh, Hadi, et al.
Publicado: (2023)
DAISM: Digital Approximate In-SRAM Multiplier-based Accelerator for DNN Training and Inference
por: Sonnino, Lorenzo, et al.
Publicado: (2023)
por: Sonnino, Lorenzo, et al.
Publicado: (2023)
SNIP: An Adaptive Mixed Precision Framework for Subbyte Large Language Model Training
por: Pan, Yunjie, et al.
Publicado: (2026)
por: Pan, Yunjie, et al.
Publicado: (2026)
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration
por: Xue, Runzhen, et al.
Publicado: (2024)
por: Xue, Runzhen, et al.
Publicado: (2024)
Memory-Efficient FPGA Implementation of Stochastic Simulated Annealing
por: Shin, Duckgyu, et al.
Publicado: (2026)
por: Shin, Duckgyu, et al.
Publicado: (2026)
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
por: Katti, Prabodh, et al.
Publicado: (2023)
por: Katti, Prabodh, et al.
Publicado: (2023)
MING: An Automated CNN-to-Edge MLIR HLS framework
por: Bi, Jiahong, et al.
Publicado: (2026)
por: Bi, Jiahong, et al.
Publicado: (2026)
Sustainable Transformer Neural Network Acceleration with Stochastic Photonic Computing
por: Afifi, S., et al.
Publicado: (2026)
por: Afifi, S., et al.
Publicado: (2026)
KLLM: Fast LLM Inference with K-Means Quantization
por: Wu, Xueying, et al.
Publicado: (2025)
por: Wu, Xueying, et al.
Publicado: (2025)
ARTEMIS: A Mixed Analog-Stochastic In-DRAM Accelerator for Transformer Neural Networks
por: Afifi, Salma, et al.
Publicado: (2024)
por: Afifi, Salma, et al.
Publicado: (2024)
FORTALESA: Fault-Tolerant Reconfigurable Systolic Array for DNN Inference
por: Cherezova, Natalia, et al.
Publicado: (2025)
por: Cherezova, Natalia, et al.
Publicado: (2025)
Efficient and Mathematically Robust Operations for Certified Neural Networks Inference
por: Geyer, Fabien, et al.
Publicado: (2024)
por: Geyer, Fabien, et al.
Publicado: (2024)
Data-Rate-Aware High-Speed CNN Inference on FPGAs
por: Habermann, Tobias, et al.
Publicado: (2026)
por: Habermann, Tobias, et al.
Publicado: (2026)
Huff-LLM: End-to-End Lossless Compression for Efficient LLM Inference
por: Yubeaton, Patrick, et al.
Publicado: (2025)
por: Yubeaton, Patrick, et al.
Publicado: (2025)
VeriCache: Turning Lossy KV Cache into Lossless LLM Inference
por: Yao, Jiayi, et al.
Publicado: (2026)
por: Yao, Jiayi, et al.
Publicado: (2026)
Large Language Model Inference Acceleration: A Comprehensive Hardware Perspective
por: Li, Jinhao, et al.
Publicado: (2024)
por: Li, Jinhao, et al.
Publicado: (2024)
PolyThrottle: Energy-efficient Neural Network Inference on Edge Devices
por: Yan, Minghao, et al.
Publicado: (2023)
por: Yan, Minghao, et al.
Publicado: (2023)
Characterization and Mitigation of Training Instabilities in Microscaling Formats
por: Su, Huangyuan, et al.
Publicado: (2025)
por: Su, Huangyuan, et al.
Publicado: (2025)
LOTION: Smoothing the Optimization Landscape for Quantized Training
por: Kwun, Mujin, et al.
Publicado: (2025)
por: Kwun, Mujin, et al.
Publicado: (2025)
AI Accelerators for Large Language Model Inference: Architecture Analysis and Scaling Strategies
por: Sharma, Amit
Publicado: (2025)
por: Sharma, Amit
Publicado: (2025)
COBRA: Algorithm-Architecture Co-optimized Binary Transformer Accelerator for Edge Inference
por: Qiao, Ye, et al.
Publicado: (2025)
por: Qiao, Ye, et al.
Publicado: (2025)
FPGA Co-Design for Efficient N:M Sparse and Quantized Model Inference
por: Hsieh, Fen-Yu, et al.
Publicado: (2025)
por: Hsieh, Fen-Yu, et al.
Publicado: (2025)
Context-Aware Mixture-of-Experts Inference on CXL-Enabled GPU-NDP Systems
por: Fan, Zehao, et al.
Publicado: (2025)
por: Fan, Zehao, et al.
Publicado: (2025)
SigmaQuant: Hardware-Aware Heterogeneous Quantization Method for Edge DNN Inference
por: Liu, Qunyou, et al.
Publicado: (2026)
por: Liu, Qunyou, et al.
Publicado: (2026)
Taming the Exponential: A Fast Softmax Surrogate for Integer-Native Edge Inference
por: Danopoulos, Dimitrios, et al.
Publicado: (2026)
por: Danopoulos, Dimitrios, et al.
Publicado: (2026)
vMCU: Coordinated Memory Management and Kernel Optimization for DNN Inference on MCUs
por: Zheng, Size, et al.
Publicado: (2024)
por: Zheng, Size, et al.
Publicado: (2024)
CHIME: Chiplet-based Heterogeneous Near-Memory Acceleration for Edge Multimodal LLM Inference
por: Chen, Yanru, et al.
Publicado: (2025)
por: Chen, Yanru, et al.
Publicado: (2025)
MixDiT: Accelerating Image Diffusion Transformer Inference with Mixed-Precision MX Quantization
por: Kim, Daeun, et al.
Publicado: (2025)
por: Kim, Daeun, et al.
Publicado: (2025)
On-Device Qwen2.5: Efficient LLM Inference with Model Compression and Hardware Acceleration
por: Xiang, Maoyang, et al.
Publicado: (2025)
por: Xiang, Maoyang, et al.
Publicado: (2025)
SOLE: Hardware-Software Co-design of Softmax and LayerNorm for Efficient Transformer Inference
por: Wang, Wenxun, et al.
Publicado: (2025)
por: Wang, Wenxun, et al.
Publicado: (2025)
PolyLUT: Ultra-low Latency Polynomial Inference with Hardware-Aware Structured Pruning
por: Andronic, Marta, et al.
Publicado: (2025)
por: Andronic, Marta, et al.
Publicado: (2025)
H2PIPE: High throughput CNN Inference on FPGAs with High-Bandwidth Memory
por: Doumet, Mario, et al.
Publicado: (2024)
por: Doumet, Mario, et al.
Publicado: (2024)
From LLM to Silicon: RL-Driven ASIC Architecture Exploration for On-Device AI Inference
por: Ganti, Ravindra, et al.
Publicado: (2026)
por: Ganti, Ravindra, et al.
Publicado: (2026)
Orion: Characterizing and Programming Apple's Neural Engine for LLM Training and Inference
por: Kumaresan, Ramchand
Publicado: (2026)
por: Kumaresan, Ramchand
Publicado: (2026)
L3: DIMM-PIM Integrated Architecture and Coordination for Scalable Long-Context LLM Inference
por: Liu, Qingyuan, et al.
Publicado: (2025)
por: Liu, Qingyuan, et al.
Publicado: (2025)
FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference
por: Hooper, Coleman, et al.
Publicado: (2025)
por: Hooper, Coleman, et al.
Publicado: (2025)
SHIELD: A Segmented Hierarchical Memory Architecture for Energy-Efficient LLM Inference on Edge NPUs
por: Zhang, Jintao, et al.
Publicado: (2026)
por: Zhang, Jintao, et al.
Publicado: (2026)
Ejemplares similares
-
Full-Stack Optimization for CAM-Only DNN Inference
por: de Lima, João Paulo C., et al.
Publicado: (2024) -
Efficient In-Memory Acceleration of Sparse Block Diagonal LLMs
por: de Lima, João Paulo Cardoso, et al.
Publicado: (2025) -
Leveraging Highly Approximated Multipliers in DNN Inference
por: Zervakis, Georgios, et al.
Publicado: (2024) -
Performance Analysis of DNN Inference/Training with Convolution and non-Convolution Operations
por: Esmaeilzadeh, Hadi, et al.
Publicado: (2023) -
DAISM: Digital Approximate In-SRAM Multiplier-based Accelerator for DNN Training and Inference
por: Sonnino, Lorenzo, et al.
Publicado: (2023)