2 citations · 2 across the 19 of their papers we have counts for
6 papers · 1 filter
Accelerating Dynamic Image Graph Construction on FPGA for Vision GNNs
Anvitha Ramachandran, Dhruv Parikh, Viktor Prasanna
Vision Graph Neural Networks (Vision GNNs, or ViGs) represent images as unstructured graphs, achieving state of the art performance in computer vision tasks such as image classific…
Context-Driven Performance Modeling for Causal Inference Operators on Neural Processing Units
Neelesh Gupta, Rakshith Jayanth, Dhruv Parikh +1
The proliferation of large language models has driven demand for long-context inference on resource-constrained edge platforms. However, deploying these models on Neural Processing…
ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs
Dhruv Parikh, Viktor Prasanna
Hyperdimensional Computing (HDC) is a brain-inspired computing paradigm that represents and manipulates information using high-dimensional vectors, called hypervectors (HV). Tradit…
Benchmarking the Performance of Large Language Models on the Cerebras Wafer Scale Engine
Zuoning Zhang, Dhruv Parikh, Youning Zhang +1
Transformer based Large Language Models (LLMs) have recently reached state of the art performance in Natural Language Processing (NLP) and Computer Vision (CV) domains. LLMs use th…
Accelerating ViT Inference on FPGA through Static and Dynamic Pruning
Dhruv Parikh, Shouyi Li, Bingyi Zhang +3
Vision Transformers (ViTs) have achieved state-of-the-art accuracy on various computer vision tasks. However, their high computational complexity prevents them from being applied t…
Performance of Graph Neural Networks for Point Cloud Applications
Dhruv Parikh, Bingyi Zhang, Rajgopal Kannan +2
Graph Neural Networks (GNNs) have gained significant momentum recently due to their capability to learn on unstructured graph data. Dynamic GNNs (DGNNs) are the current state-of-th…