13 citations · 14 across the 6 of their papers we have counts for
6 papers
Power-Performance Characterization of TinyML Systems
Yujie Zhang, Dhananjaya Wijerathne, Zhaoying Li +1
TinyML systems are enabling machine learning (ML) inference at the edge. However, there is little quantitative analysis of such systems. This paper presents a systematic performanc…
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…
TerEffic: Highly Efficient Ternary LLM Inference on FPGA
Chenyang Yin, Zhenyu Bai, Pranav Venkatram +3
Deploying Large Language Models (LLMs) efficiently on edge devices is often constrained by limited memory capacity and high power consumption. Low-bit quantization methods, particu…
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…
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…