36 citations · 84 across the 17 of their papers we have counts for
19 papers
Workhorse: Learning Robust Whole-Body Humanoid Loco-Manipulation from Human Data
Songbo Hu, Qiayuan Liao, Yufeng Chi +4
Humanoid robots still struggle to plan contact-rich whole-body manipulation from egocentric RGB and proprioception. Workhorse learns such manipulation from robot-free human demonst…
3DGS-TR: Scalable Second-Order Trust-Region Method for 3D Gaussian Splatting
Roger Hsiao, Yuchen Fang, Xiangru Huang +6
We propose 3DGS-TR,a second-order optimizer for accelerating the scene training problem in 3D Gaussian Splatting (3DGS). Unlike existing second-order approaches that rely on ex…
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…
LLM-Aided Compilation for Tensor Accelerators
Charles Hong, Sahil Bhatia, Altan Haan +4
Hardware accelerators, in particular accelerators for tensor processing, have many potential application domains. However, they currently lack the software infrastructure to suppor…
Virgo: Cluster-level Matrix Unit Integration in GPUs for Scalability and Energy Efficiency
Hansung Kim, Ruohan Richard Yan, Joshua You +2
Modern GPUs incorporate specialized matrix units such as Tensor Cores to accelerate GEMM operations, which are central to deep learning workloads. However, existing matrix unit des…
KVQuant: Towards 10 Million Context Length LLM Inference with KV Cache Quantization
Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh +4
LLMs are seeing growing use for applications which require large context windows, and with these large context windows KV cache activations surface as the dominant contributor to m…
SPEED: Speculative Pipelined Execution for Efficient Decoding
Coleman Hooper, Sehoon Kim, Hiva Mohammadzadeh +4
Generative Large Language Models (LLMs) based on the Transformer architecture have recently emerged as a dominant foundation model for a wide range of Natural Language Processing t…
Code Transpilation for Hardware Accelerators
Yuto Nishida, Sahil Bhatia, Shadaj Laddad +3
DSLs and hardware accelerators have proven to be very effective in optimizing computationally expensive workloads. In this paper, we propose a solution to the challenge of manually…
MoCA: Memory-Centric, Adaptive Execution for Multi-Tenant Deep Neural Networks
Seah Kim, Hasan Genc, Vadim Vadimovich Nikiforov +3
Driven by the wide adoption of deep neural networks (DNNs) across different application domains, multi-tenancy execution, where multiple DNNs are deployed simultaneously on the sam…
Full Stack Optimization of Transformer Inference: a Survey
Sehoon Kim, Coleman Hooper, Thanakul Wattanawong +9
Recent advances in state-of-the-art DNN architecture design have been moving toward Transformer models. These models achieve superior accuracy across a wide range of applications.…
CoSA: Scheduling by Constrained Optimization for Spatial Accelerators
Qijing Huang, Minwoo Kang, Grace Dinh +5
Recent advances in Deep Neural Networks (DNNs) have led to active development of specialized DNN accelerators, many of which feature a large number of processing elements laid out…
Efficient emotion recognition using hyperdimensional computing with combinatorial channel encoding and cellular automata
Alisha Menon, Anirudh Natarajan, Reva Agashe +5
In this paper, a hardware-optimized approach to emotion recognition based on the efficient brain-inspired hyperdimensional computing (HDC) paradigm is proposed. Emotion recognition…
Gemmini: Enabling Systematic Deep-Learning Architecture Evaluation via Full-Stack Integration
Hasan Genc, Seah Kim, Alon Amid +16
DNN accelerators are often developed and evaluated in isolation without considering the cross-stack, system-level effects in real-world environments. This makes it difficult to app…