2 citations · 5 across the 22 of their papers we have counts for
24 papers
PhysWAM: Physically Consistent World Action Model for Autonomous Driving
Dhruv Parikh, Fengcheng Yu, Quankai Gao +11
World-action models (WAMs) jointly predict how a scene will evolve and how an agent should act, however joint generation alone does not necessarily impose a shared geometric constr…
DriveReferee: Geometric Safety Verdicts Need Not Be Learned for Driving World-Action Models
Fengcheng Yu, Dhruv Parikh, Junjie Ye +5
Generative world-action models (WAMs) jointly generate future video and vehicle actions, while their action branches remain primarily optimized by expert imitation. Yet imitation p…
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…
FOLIO: Focused Semantic Memory for Streaming Video Understanding
Haoyang Fan, Dhruv Parikh, Anvitha Ramachandran +4
In online streaming video understanding, a video stream continues to arrive and queries may be issued at any time. Because streaming frames grow without bound, the system must cont…
Vision Non-Causal Trapezoidal Mamba: Eliminating Directional Scanning in Vision SSMs with Second-Order Dynamics
Anvitha Ramachandran, Dhruv Parikh, Haoyang Fan +2
State Space Models (SSMs) have emerged as an alternative to Vision Transformers, yet most vision SSMs inherit directional token scanning from causal sequence modeling. While effect…
Accurate and Resource-Efficient Federated Continual Learning
Jebacyril Arockiaraj, Dhruv Parikh, Jayashree Adivarahan +2
Federated continual learning (FCL) must learn from distributed task streams under limited resources, such as communication, computation, memory, and label availability. Existing FC…
Can Graphs Help Vision SSMs See Better?
Dhruv Parikh, Anvitha Ramachandran, Haoyang Fan +3
Vision state space models inherit the efficiency and long-range modeling ability of Mamba-style selective scans. However, their performance depends critically on the representation…
GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA
Anvitha Ramachandran, Dhruv Parikh, Viktor Prasanna
Vision Graph Neural Networks (ViGs) represent an image as a graph of patch tokens, enabling adaptive, feature-driven neighborhoods. Unlike CNNs with fixed grid biases or Vision Tra…
ImageHD: Energy-Efficient On-Device Continual Learning of Visual Representations via Hyperdimensional Computing
Jebacyril Arockiaraj, Dhruv Parikh, Viktor Prasanna
On-device continual learning (CL) is critical for edge AI systems operating on non-stationary data streams, but most existing methods rely on backpropagation or exemplar-heavy clas…
Latent Denoising Improves Visual Alignment in Large Multimodal Models
Dhruv Parikh, Jacob Fein-Ashley, Rajgopal Kannan +1
Large Multimodal Models (LMMs) such as LLaVA are typically trained with an autoregressive language modeling objective, providing only indirect supervision to visual tokens. This of…
SWE-Milestone: Evaluating AI Agents on Continuous Software Evolution
Gangda Deng, Zhaoling Chen, Zhongming Yu +11
Real-world software must continuously evolve to meet ever-changing and open-ended requirements. AI agents, increasingly deployed as long-running systems, are now entrusted to drive…
ConsensusDrop: Fusing Visual and Cross-Modal Saliency for Efficient Vision Language Models
Dhruv Parikh, Haoyang Fan, Rajgopal Kannan +1
Vision-Language Models (VLMs) are expensive because the LLM processes hundreds of largely redundant visual tokens. Existing token reduction methods typically exploit \textit{either…
Primitive-Driven Acceleration of Hyperdimensional Computing for Real-Time Image Classification
Dhruv Parikh, Jebacyril Arockiaraj, Viktor Prasanna
Hyperdimensional Computing (HDC) represents data using extremely high-dimensional, low-precision vectors, termed hypervectors (HVs), and performs learning and inference through lig…
Efficient and Accurate Graph Classification with Hyperdimensional Computing on FPGA
Jebacyril Arockiaraj, Dhruv Parikh, Viktor Prasanna
Real-time, energy-efficient inference on edge devices is essential for graph classification across a range of applications. Hyperdimensional Computing (HDC) is a brain-inspired com…
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…
Mixture of Thoughts: Learning to Aggregate What Experts Think, Not Just What They Say
Jacob Fein-Ashley, Dhruv Parikh, Rajgopal Kannan +1
Open-source Large Language Models (LLMs) increasingly specialize by domain (e.g., math, code, general reasoning), motivating systems that leverage complementary strengths across mo…
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…
ClusterViG: Efficient Globally Aware Vision GNNs via Image Partitioning
Dhruv Parikh, Jacob Fein-Ashley, Tian Ye +2
Convolutional Neural Networks (CNN) and Vision Transformers (ViT) have dominated the field of Computer Vision (CV). Graph Neural Networks (GNN) have performed remarkably well acros…
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…
Vision Transformers for End-to-End Vision-Based Quadrotor Obstacle Avoidance
Anish Bhattacharya, Nishanth Rao, Dhruv Parikh +5
We demonstrate the capabilities of an attention-based end-to-end approach for high-speed vision-based quadrotor obstacle avoidance in dense, cluttered environments, with comparison…
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…
VTR: An Optimized Vision Transformer for SAR ATR Acceleration on FPGA
Sachini Wickramasinghe, Dhruv Parikh, Bingyi Zhang +3
Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) is a key technique used in military applications like remote-sensing image recognition. Vision Transformers (ViTs)…
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…