Ang Li
Assistant Professor
Designs computer architectures and reconfigurable hardware for efficient edge and embodied AI.
Ang Li's lab at UW works on accelerators, chips & architecture and efficient training & inference. 13 papers since 2023, 7 at top venues, mostly ICLR and NeurIPS. 5 PhD students and 5 master's students. 15 alumni. Applications close .
At a glance
- papers, 2023–2026
- 3/ year
- at T1 venues
- 54%
- PhD students
- 5
- MS students
- 5
- alumni listed
- 15
Papers by yearfrom the lab homepage, 2023–2026
- 10 papers2026 · T1 4 · collecting
- 3 papers2025 · T1 3
- 0 papers2024 · T1 0
- 0 papers2023 · T1 0
T1 venuesOtherVenues: ICLR, NeurIPS, HPCA, ISCA, MICRO
Research focuspaper titles that use each topic's words
- Efficient training & inference3 papers
- reconfigurable3 papers
- Accelerators, chips & architecture1 paper
- designs computer0 papers
- computer architectures0 papers
Paper list
From the lab homepage.
202610 papers
- RFIDAll-Digital Bluetooth Low Energy (BLE) Backscatter ASIC using Standard I/O Pad Drivers in 180 nm CMOS
- MICRO (T1)CacheFlex: Direct Software-Managed Access to Higher-Level Cache for Scalable Vector Support
- A3 WorkshopCHIALU: A Comprehensive ALU Generation and Optimization Library
- ISCA (T1)DICE: Enabling Efficient General-Purpose SIMT Execution with Statically Scheduled Coarse-Grained Reconfigurable Arrays
- OSCAR Workshop PosterDORA: Open-Source Infrastructure for Prototyping Reconfigurable Fabrics
- NeurIPS (T1)HierSVA: A Data Synthesis Pipeline, Dataset, and Benchmark for LLM-Driven Hierarchical Hardware Formal Verification
- ICCDMFSA: A Multi-Format Systolic Array with Native Micro Scaling Support
- NeurIPS (T1)Step-dLLM: Adaptive Step-aware Sparse Attention for Efficient Diffusion LLM Inference
- LATTE WorkshopSTEP: Spatially Threaded Execution Pipeline
- FCCMTransDot: An Area-efficient Reconfigurable Floating-Point Unit for Trans-Precision Dot-Product Accumulation for FPGA AI Engines
20253 papers
- HPCA (T1)Exploring the Performance Improvement of Tensor Processing Engines through Transformation in the Bit-weight Dimension of MACs
- ICLR (T1)Local Linear Attention: An Optimal Interpolation of Linear and Softmax Attention For Test-Time Regression
- ICLR (T1)Tactic: Adaptive Sparse Attention with Clustering and Distribution Fitting for Long-Context LLMs
Where alumni went15 listed
Apply to UWFall 2027
UW Allen School Computer Science PhD
- Round
- Single round
- Opens
- GRE
- not required
- English
- See program page
- Fee
- See program page
Not yet confirmed for this intake.
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Your notes
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Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
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