Author

Qijun Zhang

3 papers indexed here

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Preprint Aug 2026

G-Power: Architecture-level GPU Power Modeling with Aggregated Knowledge Foundations from Known GPUs

Graphics Processing Units (GPUs) have been serving as critical computation resources for large-scale parallel computations. With increasing chip complexity, power efficiency has become an important design objective for modern GPUs. GPU power optimization relies on fast power evaluation, requiring architecture-level GPU...

Qi-Jun Zhang, Yao Lu, Shang Liu et al. · 0 citations

Preprint Aug 2026

FSGen: Agile Fused and Sparse Accelerator Generator with Accurate Power Model for LLM Applications

With the growing demand of artificial intelligence (AI) applications, large language models (LLMs) have become important workloads in many domains. The question of how to efficiently generate optimal AI chip accelerator designs remains unresolved and challenging. Currently, there is a lack of end-to-end design methodol...

J. Mok, Qi-Jun Zhang, Zhi-Yao Xie · 0 citations

Open access Aug 2026

OSCAR: An Open-Source Flexible and Hierarchical AI Accelerator Generator with Accurate Power Model

A novel open-source framework named OSCAR is proposed, which, given a set of hardware and workload specifications, provides architecture-level power estimation and can also automatically generate Chisel and synthesizable RTL of the custom AI chip.

J. Mok, Qi-Jun Zhang, Di Pang et al. · ACM Transactions on Design A... · 0 citations