publikationen

2027

1. HPCA

   Precision As You Need: A Stochastic-Computing Mixture-of-Precision Neural Network Accelerator

   Haoran Jin^*, Barry Lyu^*, Kangqi Zhang, Jirong Yang, Soren Walther, Jonathan Postelnik, Qiuyi Ding, Justin Shin, Cheng Chiu, Aaron Xiong, and Nathaniel Bleier

   In IEEE International Symposium on High-Performance Computer Architecture (HPCA), 2027

2026

1. NeurIPS

   Precision As You Need: Stochastic Computing Is a Dense Adaptive Quantizer

   Haoran Jin^*, Kangqi Zhang^*, Jirong Yang, Barry Lyu, Qiuyi Ding, Ruijie Gao, and Nathaniel Bleier

   In Advances in Neural Information Processing Systems (NeurIPS), 2026

   Abs arXiv

   Matrix multiplications dominate the inference cost of modern transformer-based vision models, yet existing efficiency techniques such as post-training quantization and mixed-precision inference are largely limited to the small set of fixed-width formats (INT4, INT8, BF16, and FP16) supported by conventional accelerators. We revisit stochastic computing (SC) as a way to lift this constraint: viewed as a dense adaptive quantizer, SC controls precision by bit-stream length L rather than a fixed datapath, while each multiplication reduces to a single AND/XNOR gate. We build a GPU library that emulates SC matrix multiplication at scale, exposes stream lengths as first-class kernel arguments, and evaluates SC end-to-end on image classification, object detection and instance segmentation, class-conditional image generation, and visual world-model planning. On top of this substrate, we develop a dynamic per-row mixed-precision policy that assigns stream length per token or group at matched average budget, requires no retraining, and uses the same SC hardware across schedules. Across tasks, SC remains competitive with fixed-format INT quantization at matched bit budgets, while per-row mixed precision helps maintain accuracy at lower average stream lengths. These results provide software-level feasibility evidence that SC can serve as a dense-precision substrate for fine-grained mixed-precision inference on modern vision transformers.

2. ICCAD

   TurnRL: Reinforcement-Learning Driven Turn-Restricted Adaptive Routing for Chiplet-Based System

   Kangqi Zhang^*, J. Wang^*, Ronald Dreslinski, and Nathaniel Bleier

   In IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2026
3. MICRO

   Fengshui: Demystifying Chiplet Ecosystem and Bespoke Neural Network Accelerator Codesign

   Haoran Jin, Jirong Yang, Zhiheng Zhang, Justin Shin, Barry Lyu, Kangqi Zhang, Yunpeng Liu, and Nathaniel Bleier

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

   arXiv
4. arXiv

   Double-P: Hierarchical Top-P Sparse Attention for Long-Context LLMs

   Wentao Ni, Kangqi Zhang, Zhongming Yu, O. Nelson, M. Lee, H. Cai, F. Porikli, J. Kim, Zhijian Liu, and Jishen Zhao

   arXiv preprint arXiv:2602.05191, 2026

   arXiv
5. arXiv

   FrontierPhysics: Evaluating Agents for End-to-End Physics Research

   Bingran You, Wenjun Ke, ..., Kangqi Zhang, and others

   arXiv preprint arXiv:2611.06910, 2026

   arXiv
6. arXiv

   Agents’ Last Exam

   Yiyou Sun, Xinyang Han, ..., Kangqi Zhang, and others

   arXiv preprint arXiv:2606.05405, 2026

   arXiv