28 citations · 40 across the 8 of their papers we have counts for
8 papers
ROSA: A Robotics Foundation Model Serving System for Robot Factories
Wenqi Jiang, Jason Clemons, Rowland O'Flaherty +5
Robotics foundation models (RFMs) are making general-purpose robots increasingly practical for factory deployments. While RFM serving systems are central to this vision, existing s…
SOLAR: AI-Powered Speed-of-Light Performance Analysis
Qijing Huang, Sana Damani, Zhifan Ye +9
How fast could a deep-learning model run on target hardware, and how far is today's implementation from that limit? These questions are central to software, hardware, and algorithm…
How Fast Can I Run My VLA? Demystifying VLA Inference Performance with VLA-Perf
Wenqi Jiang, Jason Clemons, Karu Sankaralingam +1
Vision-Language-Action (VLA) models have recently demonstrated impressive capabilities across various embodied AI tasks. While deploying VLA models on real-world robots imposes str…
HELIOS: Adaptive Model And Early-Exit Selection for Efficient LLM Inference Serving
Avinash Kumar, Shashank Nag, Jason Clemons +2
Early-Exit Large Language Models (EE-LLMs) enable high throughput inference by allowing tokens to exit early at intermediate layers. However, their throughput is limited by the com…
Vision Transformer Computation and Resilience for Dynamic Inference
Kavya Sreedhar, Jason Clemons, Rangharajan Venkatesan +2
State-of-the-art deep learning models for computer vision tasks are based on the transformer architecture and often deployed in real-time applications. In this scenario, the resour…
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training
Maohua Zhu, Jason Clemons, Jeff Pool +3
Exploiting sparsity enables hardware systems to run neural networks faster and more energy-efficiently. However, most prior sparsity-centric optimization techniques only accelerate…
Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU
Mohammad Babaeizadeh, Iuri Frosio, Stephen Tyree +2
We introduce a hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming…
vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design
Minsoo Rhu, Natalia Gimelshein, Jason Clemons +2
The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This…