36 citations · 84 across the 17 of their papers we have counts for
5 papers · 1 filter
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…
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.…
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…