8 papers
HPIM: Heterogeneous Processing-In-Memory-based Accelerator for Large Language Models Inference
Cenlin Duan, Jianlei Yang, Rubing Yang +8
The deployment of large language models (LLMs) presents significant challenges due to their enormous memory footprints, low arithmetic intensity, and stringent latency requirements…
GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search
Ao Zhou, Jianlei Yang, Tong Qiao +4
Graph Neural Networks (GNNs) have emerged as the state-of-the-art graph learning method. However, achieving efficient GNN inference on edge devices poses significant challenges, li…
CIMinus: Empowering Sparse DNN Workloads Modeling and Exploration on SRAM-based CIM Architectures
Yingjie Qi, Jianlei Yang, Rubing Yang +5
Compute-in-memory (CIM) has emerged as a pivotal direction for accelerating workloads in the field of machine learning, such as Deep Neural Networks (DNNs). However, the effective…
MIREDO: MIP-Driven Resource-Efficient Dataflow Optimization for Computing-in-Memory Accelerator
Xiaolin He, Cenlin Duan, Yingjie Qi +2
Computing-in-Memory (CIM) architectures have emerged as a promising solution for accelerating Deep Neural Networks (DNNs) by mitigating data movement bottlenecks. However, realizin…
ACE-GNN: Adaptive GNN Co-Inference with System-Aware Scheduling in Dynamic Edge Environments
Ao Zhou, Jianlei Yang, Tong Qiao +5
The device-edge co-inference paradigm effectively bridges the gap between the high resource demands of Graph Neural Networks (GNNs) and limited device resources, making it a promis…
Towards Affordable, Adaptive and Automatic GNN Training on CPU-GPU Heterogeneous Platforms
Tong Qiao, Ao Zhou, Yingjie Qi +4
Graph Neural Networks (GNNs) have been widely adopted due to their strong performance. However, GNN training often relies on expensive, high-performance computing platforms, limiti…