187 citations · 373 across the 10 of their papers we have counts for
12 papers
Relax: Composable Abstractions for End-to-End Dynamic Machine Learning
Ruihang Lai, Junru Shao, Siyuan Feng +16
Dynamic shape computations have become critical in modern machine learning workloads, especially in emerging large language models. The success of these models has driven the deman…
Federated Remote Physiological Measurement with Imperfect Data
Xin Liu, Mingchuan Zhang, Ziheng Jiang +2
The growing need for technology that supports remote healthcare is being acutely highlighted by an aging population and the COVID-19 pandemic. In health-related machine learning ap…
EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement
Xin Liu, Brian L. Hill, Ziheng Jiang +2
Camera-based physiological measurement is a growing field with neural models providing state-the-art-performance. Prior research have explored various "end-to-end" models; however…
Automated Backend-Aware Post-Training Quantization
Ziheng Jiang, Animesh Jain, Andrew Liu +4
Quantization is a key technique to reduce the resource requirement and improve the performance of neural network deployment. However, different hardware backends such as x86 CPU, N…
SplitSR: An End-to-End Approach to Super-Resolution on Mobile Devices
Xin Liu, Yuang Li, Josh Fromm +4
Super-resolution (SR) is a coveted image processing technique for mobile apps ranging from the basic camera apps to mobile health. Existing SR algorithms rely on deep learning mode…
MetaPhys: Few-Shot Adaptation for Non-Contact Physiological Measurement
Xin Liu, Ziheng Jiang, Josh Fromm +3
There are large individual differences in physiological processes, making designing personalized health sensing algorithms challenging. Existing machine learning systems struggle t…
Characterizing Structural Regularities of Labeled Data in Overparameterized Models
Ziheng Jiang, Chiyuan Zhang, Kunal Talwar +1
Humans are accustomed to environments that contain both regularities and exceptions. For example, at most gas stations, one pays prior to pumping, but the occasional rural station…
Just-in-Time Dynamic-Batching
Sheng Zha, Ziheng Jiang, Haibin Lin +1
Batching is an essential technique to improve computation efficiency in deep learning frameworks. While batch processing for models with static feed-forward computation graphs is s…
Relay: A High-Level Compiler for Deep Learning
Jared Roesch, Steven Lyubomirsky, Marisa Kirisame +7
Frameworks for writing, compiling, and optimizing deep learning (DL) models have recently enabled progress in areas like computer vision and natural language processing. Extending…
A Hardware-Software Blueprint for Flexible Deep Learning Specialization
Thierry Moreau, Tianqi Chen, Luis Vega +8
Specialized Deep Learning (DL) acceleration stacks, designed for a specific set of frameworks, model architectures, operators, and data types, offer the allure of high performance…
Learning to Optimize Tensor Programs
Tianqi Chen, Lianmin Zheng, Eddie Yan +5
We introduce a learning-based framework to optimize tensor programs for deep learning workloads. Efficient implementations of tensor operators, such as matrix multiplication and hi…
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
Tianqi Chen, Thierry Moreau, Ziheng Jiang +9
There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narr…