8 citations · 14 across the 9 of their papers we have counts for
9 papers
Building an Open CGRA Ecosystem for Agile Innovation
Rohan Juneja, Pranav Dangi, Thilini Kaushalya Bandara +4
Modern computing workloads, particularly in AI and edge applications, demand hardware-software co-design to meet aggressive performance and energy targets. Such co-design benefits…
Data-aware Dynamic Execution of Irregular Workloads on Heterogeneous Systems
Zhenyu Bai, Dan Wu, Pranav Dangi +3
Current approaches to scheduling workloads on heterogeneous systems with specialized accelerators often rely on manual partitioning, offloading tasks with specific compute patterns…
Nexus Machine: An Active Message Inspired Reconfigurable Architecture for Irregular Workloads
Rohan Juneja, Pranav Dangi, Thilini Kaushalya Bandara +2
Modern reconfigurable architectures are increasingly favored for resource-constrained edge devices as they balance high performance, energy efficiency, and programmability well. Ho…
Hybrid Photonic-digital Accelerator for Attention Mechanism
Huize Li, Dan Chen, Tulika Mitra
The wide adoption and substantial computational resource requirements of attention-based Transformers have spurred the demand for efficient hardware accelerators. Unlike digital-ba…
Enhancing CGRA Efficiency Through Aligned Compute and Communication Provisioning
Zhaoying Li, Pranav Dangi, Chenyang Yin +5
Coarse-grained Reconfigurable Arrays (CGRAs) are domain-agnostic accelerators that enhance the energy efficiency of resource-constrained edge devices. The CGRA landscape is diverse…
Sustainable Hardware Specialization
Pranav Dangi, Thilini Kaushalya Bandara, Saeideh Sheikhpour +2
Hardware specialization is commonly viewed as a way to scale performance in the dark silicon era with modern-day SoCs featuring multiple tens of dedicated accelerators. By only pow…
SWAT: Scalable and Efficient Window Attention-based Transformers Acceleration on FPGAs
Zhenyu Bai, Pranav Dangi, Huize Li +1
Efficiently supporting long context length is crucial for Transformer models. The quadratic complexity of the self-attention computation plagues traditional Transformers. Sliding w…
InkStream: Real-time GNN Inference on Streaming Graphs via Incremental Update
Dan Wu, Zhaoying Li, Tulika Mitra
Classic Graph Neural Network (GNN) inference approaches, designed for static graphs, are ill-suited for streaming graphs that evolve with time. The dynamism intrinsic to streaming…
Accelerating Edge AI with Morpher: An Integrated Design, Compilation and Simulation Framework for CGRAs
Dhananjaya Wijerathne, Zhaoying Li, Tulika Mitra
Coarse-Grained Reconfigurable Arrays (CGRAs) hold great promise as power-efficient edge accelerator, offering versatility beyond AI applications. Morpher, an open-source, architect…