Learning Like Humans: Resource-Efficient Federated Fine-Tuning through Cognitive Developmental Stages
"边缘设备" 相关文章
From LLMs to Edge: Parameter-Efficient Fine-Tuning on Edge Devices
Lightweight Remote Sensing Scene Classification on Edge Devices via Knowledge Distillation and Early-exit
Enhancing Large Multimodal Models with Adaptive Sparsity and KV Cache Compression
DeltaLLM: A Training-Free Framework Exploiting Temporal Sparsity for Efficient Edge LLM Inference
Toward a Real-Time Framework for Accurate Monocular 3D Human Pose Estimation with Geometric Priors
Quantization-Aware Neuromorphic Architecture for Efficient Skin Disease Classification on Resource-Constrained Devices
Performance Analysis of Post-Training Quantization for CNN-based Conjunctival Pallor Anemia Detection
DFQ-ViT: Data-Free Quantization for Vision Transformers without Fine-tuning
An ultra-low-power CGRA for accelerating Transformers at the edge
PRISM: Distributed Inference for Foundation Models at Edge
Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge
微软全新 Phi-4-mini-flash-reasoning 模型将AI设备速度提升10倍
mGRADE: Minimal Recurrent Gating Meets Delay Convolutions for Lightweight Sequence Modeling
Liquid AI推出新“液态”边缘模型Hyena Edge，实测效率质量均超Transformer
疑似僵尸网络使用暴力攻击瞄准边缘设备
五眼联盟发布网络边缘设备安全指南，强调取证可见性
Microsoft AI Researchers Introduce Advanced Low-Bit Quantization Techniques to Enable Efficient LLM Deployment on Edge Devices without High Computational Costs
OpenBMB Just Released MiniCPM-o 2.6: A New 8B Parameters, Any-to-Any Multimodal Model that can Understand Vision, Speech, and Language and Runs on Edge Devices
手机也能玩RAG？谷歌EdgeRAG做到了~
