Yimin Gao

Yimin Gao

Publications

You can also find my articles on my Google Scholar profile.

Conference Papers

FlexPosit: Tunable Fractional Precision for LLM Inference Accelerators

Published in IEEE/ACM International Symposium on Microarchitecture (MICRO), 2026

To appear. Posit-based algorithm-hardware co-design that treats precision as a continuously tunable resource: distribution-aware mixed-precision quantization paired with a unified bit-serial systolic array tunable from 4 to 8 bits at fully regular dataflow.

Recommended citation: Y. Gao, L. Dai, J. Yin, X. Guo, and M. R. Stan. (2026). "FlexPosit: Tunable Fractional Precision for LLM Inference Accelerators." IEEE/ACM International Symposium on Microarchitecture (MICRO). To appear.

EAS-CiM 2.0: Event-driven Asynchronous Stream-based Compute-in-Memory Kernels with Scalable Precision

Published in IEEE International Symposium on Circuits and Systems (ISCAS), 2025

Event-driven asynchronous stream-based compute-in-memory kernels supporting scalable precision.

Recommended citation: R. Sreekumar, M. Morsali, N. Solomon, Y. Gao, M. Park, K. Lee, et al. (2025). "EAS-CiM 2.0: Event-driven Asynchronous Stream-based Compute-in-Memory Kernels with Scalable Precision." IEEE International Symposium on Circuits and Systems (ISCAS).
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CryptoPUF: A Lightweight and ML-Resilient Strong PUF Based on a Weak PUF and Crypto Core

Published in IEEE International Conference on RFID Technology and Applications (RFID-TA), 2024

A lightweight strong PUF construction resilient to machine-learning modeling attacks, built from a weak PUF and a crypto core.

Recommended citation: Y. Gao, J. Chilaka, E. Pantoja, R. Klenke, and M. R. Stan. (2024). "CryptoPUF: A Lightweight and ML-Resilient Strong PUF Based on a Weak PUF and Crypto Core." IEEE International Conference on RFID Technology and Applications (RFID-TA).
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A Feedback Self-Adaptive Body Biasing-Based RF-DC Rectifier for Highly-Sensitive RF Energy Harvesting

Published in IEEE International Symposium on Circuits and Systems (ISCAS), 2024

A self-adaptive body-biasing RF-DC rectifier design for highly sensitive RF energy harvesting.

Recommended citation: J. Yin, E. Pantoja, Y. Gao, and M. R. Stan. (2024). "A Feedback Self-Adaptive Body Biasing-Based RF-DC Rectifier for Highly-Sensitive RF Energy Harvesting." IEEE International Symposium on Circuits and Systems (ISCAS).
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LiteAIR5: A System-Level Framework for the Design and Modeling of AI-Extended RISC-V Cores

Published in IEEE International System-on-Chip Conference (SOCC), 2023

A system-level framework for designing and modeling RISC-V cores extended with AI acceleration.

Recommended citation: Y. Gao, S. Mosanu, M. N. Sakib, V. Verma, X. Guo, and M. R. Stan. (2023). "LiteAIR5: A System-Level Framework for the Design and Modeling of AI-Extended RISC-V Cores." IEEE International System-on-Chip Conference (SOCC).
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Design Space Exploration of Layer-wise Mixed-Precision Quantization with Tightly Integrated Edge Inference Units

Published in Great Lakes Symposium on VLSI (GLSVLSI), 2023

Design space exploration of layer-wise mixed-precision quantization paired with tightly integrated edge inference units.

Recommended citation: X. Zhao, Y. Gao, V. Verma, R. Xu, M. R. Stan, and X. Guo. (2023). "Design Space Exploration of Layer-wise Mixed-Precision Quantization with Tightly Integrated Edge Inference Units." Great Lakes Symposium on VLSI (GLSVLSI).

Journal Articles

Membrane: Accelerating Database Analytics with DRAM-Based PIM Filtering and Schema Denormalization

Published in ACM Transactions on Architecture and Code Optimization (TACO), 2025

DRAM-based processing-in-memory filtering combined with schema denormalization to accelerate database analytics.

Recommended citation: A. Shekar, K. Gaffney, M. Prammer, K. Kiyawat, L. Wu, H. Caminal, Z. Fan, Y. Gao, et al. (2025). "Membrane: Accelerating Database Analytics with DRAM-Based PIM Filtering and Schema Denormalization." ACM Transactions on Architecture and Code Optimization (TACO).
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Edge-MPQ: Layer-wise Mixed-Precision Quantization with Tightly Integrated Versatile Inference Units for Edge Computing

Published in IEEE Transactions on Computers, 2024

Layer-wise mixed-precision quantization co-designed with versatile inference units for edge inference.

Recommended citation: X. Zhao, R. Xu, Y. Gao, V. Verma, M. R. Stan, and X. Guo. (2024). "Edge-MPQ: Layer-wise Mixed-Precision Quantization with Tightly Integrated Versatile Inference Units for Edge Computing." IEEE Transactions on Computers.
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Patents