3 citations · 4 across the 11 of their papers we have counts for
12 papers
Activation Sparsity with Weight Approximation for Faster LLM Decoding on Offloaded Weights
JuneHyung Kim, Sankeerth Durvasula, Nandita Vijaykumar
Deploying LLMs on consumer-grade GPUs with insufficient memory to hold their weights can result in prohibitively slow inference, because decoding repeatedly transfers offloaded wei…
XPR: An Extensible Cross-Platform Point-Based Differentiable Renderer
Steve Rhyner, Sankeerth Durvasula, Aleksandr Kovalev +7
Point-based differentiable rendering underpins modern 3D reconstruction, novel-view synthesis, and learning-based graphics pipelines, but developing new rendering methods often req…
DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures
Peiming Yang, Sankeerth Durvasula, Ivan Fernandez +4
High-performance Host processors can integrate Processing-In-Memory (PIM) devices, which can accelerate memory-intensive kernels of Machine Learning (ML) models, including Large La…
ContraGS: Codebook-Condensed and Trainable Gaussian Splatting for Fast, Memory-Efficient Reconstruction
Sankeerth Durvasula, Sharanshangar Muhunthan, Zain Moustafa +7
3D Gaussian Splatting (3DGS) is a state-of-art technique to model real-world scenes with high quality and real-time rendering. Typically, a higher quality representation can be ach…
FG-Attn: Leveraging Fine-Grained Sparse Attention in Video Diffusion Models
Sankeerth Durvasula, Kavya Sreedhar, Zain Moustafa +6
Using diffusion transformers for media generation may require evaluating attention over extremely long sequences, with attention layers accounting for the majority of generation la…
Squeeze3D: Extreme Neural Compression with Latent Space Bridging
Rishit Dagli, Yushi Guan, Sankeerth Durvasula +2
We propose Squeeze3D, a novel framework that leverages implicit prior knowledge learnt by existing pre-trained encoders and decoders to compress 3D data at extremely high compressi…
DISORF: A Distributed Online 3D Reconstruction Framework for Mobile Robots
Chunlin Li, Hanrui Fan, Xiaorui Huang +3
We present a framework, DISORF, to enable online 3D reconstruction and visualization of scenes captured by resource-constrained mobile robots and edge devices. To address the limit…
PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures
Christina Giannoula, Peiming Yang, Ivan Fernandez +7
Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kerne…
ACS: Concurrent Kernel Execution on Irregular, Input-Dependent Computational Graphs
Sankeerth Durvasula, Adrian Zhao, Raymond Kiguru +3
GPUs are widely used to accelerate many important classes of workloads today. However, we observe that several important emerging classes of workloads, including simulation engines…
DISTWAR: Fast Differentiable Rendering on Raster-based Rendering Pipelines
Sankeerth Durvasula, Adrian Zhao, Fan Chen +3
Differentiable rendering is a technique used in an important emerging class of visual computing applications that involves representing a 3D scene as a model that is trained from 2…
EvConv: Fast CNN Inference on Event Camera Inputs For High-Speed Robot Perception
Sankeerth Durvasula, Yushi Guan, Nandita Vijaykumar
Event cameras capture visual information with a high temporal resolution and a wide dynamic range. This enables capturing visual information at fine time granularities (e.g., micro…
VoxelCache: Accelerating Online Mapping in Robotics and 3D Reconstruction Tasks
Sankeerth Durvasula, Raymond Kiguru, Samarth Mathur +3
Real-time 3D mapping is a critical component in many important applications today including robotics, AR/VR, and 3D visualization. 3D mapping involves continuously fusing depth map…