6 papers
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…
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…
Efficient SRAM-PIM Co-design by Joint Exploration of Value-Level and Bit-Level Sparsity
Cenlin Duan, Jianlei Yang, Yikun Wang +7
Processing-in-memory (PIM) is a transformative architectural paradigm designed to overcome the Von Neumann bottleneck. Among PIM architectures, digital SRAM-PIM emerges as a promis…
CIMFlow: An Integrated Framework for Systematic Design and Evaluation of Digital CIM Architectures
Yingjie Qi, Jianlei Yang, Yiou Wang +6
Digital Compute-in-Memory (CIM) architectures have shown great promise in Deep Neural Network (DNN) acceleration by effectively addressing the "memory wall" bottleneck. However, th…
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…
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…