5 papers
A 16 nm 1.60TOPS/W High Utilization DNN Accelerator with 3D Spatial Data Reuse and Efficient Shared Memory Access
Xiaoling Yi, Ryan Antonio, Yunhao Deng +4
Achieving high compute utilization across a wide range of AI workloads is crucial for the efficiency of versatile DNN accelerators. This paper presents the Voltra chip and its util…
Torrent: A Distributed DMA for Efficient and Flexible Point-to-Multipoint Data Movement
Yunhao Deng, Fanchen Kong, Xiaoling Yi +2
The growing disparity between computational power and on-chip communication bandwidth is a critical bottleneck in modern Systems-on-Chip (SoCs), especially for data-parallel worklo…
DataMaestro: A Versatile and Efficient Data Streaming Engine Bringing Decoupled Memory Access To Dataflow Accelerators
Xiaoling Yi, Yunhao Deng, Ryan Antonio +3
Deep Neural Networks (DNNs) have achieved remarkable success across various intelligent tasks but encounter performance and energy challenges in inference execution due to data mov…
An Open-Source HW-SW Co-Development Framework Enabling Efficient Multi-Accelerator Systems
Ryan Albert Antonio, Joren Dumoulin, Xiaoling Yi +4
Heterogeneous accelerator-centric compute clusters are emerging as efficient solutions for diverse AI workloads. However, current integration strategies often compromise data movem…
XDMA: A Distributed, Extensible DMA Architecture for Layout-Flexible Data Movements in Heterogeneous Multi-Accelerator SoCs
Fanchen Kong, Yunhao Deng, Xiaoling Yi +2
As modern AI workloads increasingly rely on heterogeneous accelerators, ensuring high-bandwidth and layout-flexible data movements between accelerator memories has become a pressin…