219 citations · 284 across the 7 of their papers we have counts for
7 papers
Adaptive Parallel Monte Carlo Tree Search for Efficient Test-time Compute Scaling
Hongbeen Kim, Juhyun Lee, Sanghyeon Lee +2
Monte Carlo Tree Search (MCTS) is an effective test-time compute scaling (TTCS) method for improving the reasoning performance of large language models, but its highly variable exe…
Scaling On-Device GPU Inference for Large Generative Models
Jiuqiang Tang, Raman Sarokin, Ekaterina Ignasheva +5
Driven by the advancements in generative AI, large machine learning models have revolutionized domains such as image processing, audio synthesis, and speech recognition. While serv…
Speed Is All You Need: On-Device Acceleration of Large Diffusion Models via GPU-Aware Optimizations
Yu-Hui Chen, Raman Sarokin, Juhyun Lee +4
The rapid development and application of foundation models have revolutionized the field of artificial intelligence. Large diffusion models have gained significant attention for th…
Efficient Heterogeneous Video Segmentation at the Edge
Jamie Menjay Lin, Siargey Pisarchyk, Juhyun Lee +7
We introduce an efficient video segmentation system for resource-limited edge devices leveraging heterogeneous compute. Specifically, we design network models by searching across m…
Efficient Memory Management for Deep Neural Net Inference
Yury Pisarchyk, Juhyun Lee
While deep neural net inference was considered a task for servers only, latest advances in technology allow the task of inference to be moved to mobile and embedded devices, desire…
On-Device Neural Net Inference with Mobile GPUs
Juhyun Lee, Nikolay Chirkov, Ekaterina Ignasheva +6
On-device inference of machine learning models for mobile phones is desirable due to its lower latency and increased privacy. Running such a compute-intensive task solely on the mo…
MediaPipe: A Framework for Building Perception Pipelines
Camillo Lugaresi, Jiuqiang Tang, Hadon Nash +11
Building applications that perceive the world around them is challenging. A developer needs to (a) select and develop corresponding machine learning algorithms and models, (b) buil…