1 citations · 1 across the 7 of their papers we have counts for
7 papers
GNNavigator: Towards Adaptive Training of Graph Neural Networks via Automatic Guideline Exploration
Tong Qiao, Jianlei Yang, Yingjie Qi +5
Graph Neural Networks (GNNs) succeed significantly in many applications recently. However, balancing GNNs training runtime cost, memory consumption, and attainable accuracy for var…
Towards Efficient SRAM-PIM Architecture Design by Exploiting Unstructured Bit-Level Sparsity
Cenlin Duan, Jianlei Yang, Yiou Wang +7
Bit-level sparsity in neural network models harbors immense untapped potential. Eliminating redundant calculations of randomly distributed zero-bits significantly boosts computatio…
Graph Neural Networks Automated Design and Deployment on Device-Edge Co-Inference Systems
Ao Zhou, Jianlei Yang, Tong Qiao +4
The key to device-edge co-inference paradigm is to partition models into computation-friendly and computation-intensive parts across the device and the edge, respectively. However,…
DDC-PIM: Efficient Algorithm/Architecture Co-design for Doubling Data Capacity of SRAM-based Processing-In-Memory
Cenlin Duan, Jianlei Yang, Xiaolin He +9
Processing-in-memory (PIM), as a novel computing paradigm, provides significant performance benefits from the aspect of effective data movement reduction. SRAM-based PIM has been d…
Architectural Implications of GNN Aggregation Programming Abstractions
Yingjie Qi, Jianlei Yang, Ao Zhou +2
Graph neural networks (GNNs) have gained significant popularity due to the powerful capability to extract useful representations from graph data. As the need for efficient GNN comp…
Lossy and Lossless (L) Post-training Model Size Compression
Yumeng Shi, Shihao Bai, Xiuying Wei +2
Deep neural networks have delivered remarkable performance and have been widely used in various visual tasks. However, their huge size causes significant inconvenience for transmis…