16 citations · 39 across the 9 of their papers we have counts for
9 papers
Thread-Efficient Decoding for Neural Texture Compression
Janarbek Matai, Sho Ikeda, Lukasz Lipski +1
Neural texture compression (NTC) achieves higher compression ratios than BCn formats but suffers from GPU thread divergence, which significantly reduces runtime performance. In thi…
PEPS: Positional Encoding Projected Sampling -- Extended
Guillaume Perez, Janarbek Matai, Takahiro Harada
Implicit neural representations (INRs) are increasingly being used as tools to map coordinates to signals, encompassing applications from neural fields to texture compression, shap…
Restoring the Broken Covenant Between Compilers and Deep Learning Accelerators
Sean Kinzer, Soroush Ghodrati, Rohan Mahapatra +6
Deep learning accelerators address the computational demands of Deep Neural Networks (DNNs), departing from the traditional Von Neumann execution model. They leverage specialized h…
Comprehensive Benchmarking of Binary Neural Networks on NVM Crossbar Architectures
Ruirong Huang, Zichao Yue, Caroline Huang +2
Non-volatile memory (NVM) crossbars have been identified as a promising technology, for accelerating important machine learning operations, with matrix-vector multiplication being…
Factorized Inverse Path Tracing for Efficient and Accurate Material-Lighting Estimation
Liwen Wu, Rui Zhu, Mustafa B. Yaldiz +7
Inverse path tracing has recently been applied to joint material and lighting estimation, given geometry and multi-view HDR observations of an indoor scene. However, it has two maj…
IRISformer: Dense Vision Transformers for Single-Image Inverse Rendering in Indoor Scenes
Rui Zhu, Zhengqin Li, Janarbek Matai +2
Indoor scenes exhibit significant appearance variations due to myriad interactions between arbitrarily diverse object shapes, spatially-changing materials, and complex lighting. Sh…
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation
Hong Cai, Janarbek Matai, Shubhankar Borse +3
In this paper, we propose a novel method, X-Distill, to improve the self-supervised training of monocular depth via cross-task knowledge distillation from semantic segmentation to…
Parallel Programming for FPGAs
Ryan Kastner, Janarbek Matai, Stephen Neuendorffer
This book focuses on the use of algorithmic high-level synthesis (HLS) to build application-specific FPGA systems. Our goal is to give the reader an appreciation of the process of…
Enabling FPGAs for the Masses
Janarbek Matai, Dustin Richmond, Dajung Lee +1
Implementing an application on a FPGA remains a difficult, non-intuitive task that often requires hardware design expertise in a hardware description language (HDL). High-level syn…