5 papers · 1 filter
SpecReuse: Spectral Graph Reuse for Efficient Vision GNN Inference on FPGAs
Isabella Bernhardt Eiliya, Anvitha Ramachandran, Dhruv Parikh +1
Dynamic Image Graph Construction (DIGC) is the primary performance bottleneck in FPGA acceleration of Vision Graph Neural Networks (ViGs), reconstructing graph connectivity at ever…
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…