11 citations · 24 across the 12 of their papers we have counts for
13 papers
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…
Exploring Deep-to-Shallow Transformable Neural Networks for Intelligent Embedded Systems
Xiangzhong Luo, Weichen Liu
Thanks to the evolving network depth, convolutional neural networks (CNNs) have achieved remarkable success across various embedded scenarios, paving the way for ubiquitous embedde…
Coherence-Aware Task Graph Modeling for Realistic Application
Guochu Xiong, Xiangzhong Luo, Weichen Liu
As multicore systems continue to scale, cache coherence has emerged as a critical determinant of system performance, with coherence behavior and task execution closely intertwined,…
Learning Cache Coherence Traffic for NoC Routing Design
Guochu Xiong, Xiangzhong Luo, Weichen Liu
The rapid growth of multi-core systems highlights the need for efficient Network-on-Chip (NoC) design to ensure seamless communication. Cache coherence, essential for data consiste…
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
Xiangzhong Luo, Di Liu, Hao Kong +4
Deep neural networks (DNNs) have recently achieved impressive success across a wide range of real-world vision and language processing tasks, spanning from image classification to…
Enabling Energy-Efficient Deployment of Large Language Models on Memristor Crossbar: A Synergy of Large and Small
Zhehui Wang, Tao Luo, Cheng Liu +3
Large language models (LLMs) have garnered substantial attention due to their promising applications in diverse domains. Nevertheless, the increasing size of LLMs comes with a sign…