11 citations · 24 across the 12 of their papers we have counts for
8 papers · 1 filter
SlimKV: Joint Token-Feature KV Cache Compression with Reconstruction-Free Beacon Attention
Zihan Teng, Jiayu Zhao, Wentao Ren +4
Long-context LLM serving is increasingly bottlenecked by KV-cache memory, especially in resource-constrained scenarios. Among existing KV-cache compression strategies, token-wise m…
Mapping Without Graphs: Learning Coherence Traffic for Task Placement
Guochu Xiong, Tianrui Ma, Weichen Liu
Cache coherence is essential for communication in many-core Network-on-Chip (NoC)-based systems. As application scale and complexity increase, efficiently managing communication be…
Coherence in Control: Bridging Many-Core Mapping and Routing through Cost Unification
Guochu Xiong, Xiangzhong Luo, Weichen Liu
The rapid growth of data-intensive applications increases communication demands in many-core systems, where cache coherence, while essential for correct communication and data cons…
Towards Efficient Convolutional Neural Network for Embedded Hardware via Multi-Dimensional Pruning
Hao Kong, Di Liu, Xiangzhong Luo +5
In this paper, we propose TECO, a multi-dimensional pruning framework to collaboratively prune the three dimensions (depth, width, and resolution) of convolutional neural networks…
EvoLP: Self-Evolving Latency Predictor for Model Compression in Real-Time Edge Systems
Shuo Huai, Hao Kong, Shiqing Li +5
Edge devices are increasingly utilized for deploying deep learning applications on embedded systems. The real-time nature of many applications and the limited resources of edge dev…
CRIMP: Compact & Reliable DNN Inference on In-Memory Processing via Crossbar-Aligned Compression and Non-ideality Adaptation
Shuo Huai, Hao Kong, Xiangzhong Luo +5
Crossbar-based In-Memory Processing (IMP) accelerators achieve high-speed, low-power computing for deep neural networks (DNNs), but face three obstacles. First, floating-point (FP)…
FedTR: Federated Learning Framework with Transfer Learning for Industrial Visual Inspection
Vikash Sathiamoorthy, Shuo Huai, Hao Kong +7
Federated learning (FL) is a collaborative learning scheme to train deep learning models, where collaborating parties can consolidate their models without sharing local data with o…
BitsMoE: Cost-Aware Bit Allocation in Spectral Space for MoE LLM Quantization
Jiayu Zhao, Zihan Teng, Minhao Fan +4
Mixture-of-Experts (MoE) large language models incur substantial memory costs due to their large expert parameter counts. Mixed-precision quantization reduces these costs by alloca…