36 citations · 84 across the 17 of their papers we have counts for
7 papers · 1 filter
Ego-Vision World Model for Humanoid Contact Planning
Hang Liu, Yuman Gao, Sangli Teng +5
Enabling humanoid robots to exploit physical contact, rather than simply avoid collisions, is crucial for autonomy in unstructured environments. Traditional optimization-based plan…
DOSA: Differentiable Model-Based One-Loop Search for DNN Accelerators
Charles Hong, Qijing Huang, Grace Dinh +2
In the hardware design space exploration process, it is critical to optimize both hardware parameters and algorithm-to-hardware mappings. Previous work has largely approached this…
hdl2v: A Code Translation Dataset for Enhanced LLM Verilog Generation
Charles Hong, Brendan Roberts, Huijae An +3
Large language models (LLMs) are playing an increasingly large role in domains such as code generation, including hardware code generation, where Verilog is the key language. Howev…
Multipole Attention for Efficient Long Context Reasoning
Coleman Hooper, Sebastian Zhao, Luca Manolache +5
Large Reasoning Models (LRMs) have shown promising accuracy improvements on complex problem-solving tasks. While these models have attained high accuracy by leveraging additional c…
Autocomp: A Powerful and Portable Code Optimizer for Tensor Accelerators
Charles Hong, Sahil Bhatia, Alvin Cheung +1
Hardware accelerators, especially those designed for tensor processing, have become ubiquitous in today's computing landscape. However, even with significant efforts in building co…
FGMP: Fine-Grained Mixed-Precision Weight and Activation Quantization for Hardware-Accelerated LLM Inference
Coleman Hooper, Charbel Sakr, Ben Keller +4
Quantization is a powerful tool to improve large language model (LLM) inference efficiency by utilizing more energy-efficient low-precision datapaths and reducing memory footprint.…
ETS: Efficient Tree Search for Inference-Time Scaling
Coleman Hooper, Sehoon Kim, Suhong Moon +7
Test-time compute scaling has emerged as a new axis along which to improve model accuracy, where additional computation is used at inference time to allow the model to think longer…