15 citations · 59 across the 29 of their papers we have counts for
29 papers
ISO: An RLVR-Native Optimization Stack
Hanqing Zhu, Wenyan Cong, Zhizhou Sha +8
Reinforcement learning with verifiable rewards (RLVR) advances the reasoning capabilities of language models, yet the optimization layer that converts reward feedback into weight-s…
Geometry-Preserving Orthonormal Initialization for Low-Rank Adaptation in RLVR
Ruijia Zhang, Jiacheng Zhu, Hanqing Zhu +1
Low-rank adaptation (LoRA) and its variants enable parameter-efficient fine-tuning of large language models under the supervised fine-tuning (SFT) paradigm. However, their efficacy…
General-Purpose Photonic Computing Primitive for Contemporary Artificial Intelligence
Shupeng Ning, Chenghao Feng, Zhenxiang Xu +4
Photonic computing offers a promising route to accelerating artificial intelligence (AI) by providing high analog bandwidth, low latency, and low energy consumption. However, exist…
Harnessing Photonics for Machine Intelligence
Hanqing Zhu, Shupeng Ning, Hongjian Zhou +4
The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scal…
The Path Not Taken: RLVR Provably Learns Off the Principals
Hanqing Zhu, Zhenyu Zhang, Hanxian Huang +11
Reinforcement Learning with Verifiable Rewards (RLVR) reliably improves the reasoning performance of large language models, yet it appears to modify only a small fraction of parame…
ENLighten: Lighten the Transformer, Enable Efficient Optical Acceleration
Hanqing Zhu, Zhican Zhou, Shupeng Ning +4
Photonic computing has emerged as a promising substrate for accelerating the dense linear-algebra operations at the heart of AI, yet adoption for large Transformer models remains i…
PPAAS: PVT and Pareto Aware Analog Sizing via Goal-conditioned Reinforcement Learning
Seunggeun Kim, Ziyi Wang, Sungyoung Lee +4
Device sizing is a critical yet challenging step in analog and mixed-signal circuit design, requiring careful optimization to meet diverse performance specifications. This challeng…
Can Test-Time Scaling Improve World Foundation Model?
Wenyan Cong, Hanqing Zhu, Peihao Wang +7
World foundation models, which simulate the physical world by predicting future states from current observations and inputs, have become central to many applications in physical in…
Hardware-Efficient Photonic Tensor Core: Accelerating Deep Neural Networks with Structured Compression
Shupeng Ning, Hanqing Zhu, Chenghao Feng +3
The rapid growth in computing demands, particularly driven by artificial intelligence applications, has begun to exceed the capabilities of traditional electronic hardware. Optical…
APOLLO: SGD-like Memory, AdamW-level Performance
Hanqing Zhu, Zhenyu Zhang, Wenyan Cong +7
Large language models (LLMs) are notoriously memory-intensive during training, particularly with the popular AdamW optimizer. This memory burden necessitates using more or higher-e…
PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices
Hanqing Zhu, Wenyan Cong, Guojin Chen +4
Electromagnetic field simulation is central to designing, optimizing, and validating photonic devices and circuits. However, costly computation associated with numerical simulation…
INSIGHT: Universal Neural Simulator for Analog Circuits Harnessing Autoregressive Transformers
Souradip Poddar, Youngmin Oh, Yao Lai +3
Analog front-end design heavily relies on specialized human expertise and costly trial-and-error simulations, which motivated many prior works on analog design automation. However,…
LLM-Enhanced Bayesian Optimization for Efficient Analog Layout Constraint Generation
Guojin Chen, Keren Zhu, Seunggeun Kim +4
Analog layout synthesis faces significant challenges due to its dependence on manual processes, considerable time requirements, and performance instability. Current Bayesian Optimi…
Photonic-Electronic Integrated Circuits for High-Performance Computing and AI Accelerators
Shupeng Ning, Hanqing Zhu, Chenghao Feng +8
In recent decades, the demand for computational power has surged, particularly with the rapid expansion of artificial intelligence (AI). As we navigate the post-Moore's law era, th…
DREAMPlaceFPGA-MP: An Open-Source GPU-Accelerated Macro Placer for Modern FPGAs with Cascade Shapes and Region Constraints
Zhili Xiong, Rachel Selina Rajarathnam, Zhixing Jiang +2
FPGA macro placement plays a pivotal role in routability and timing closer to the modern FPGA physical design flow. In modern FPGAs, macros could be subject to complex cascade shap…
Integrated multi-operand optical neurons for scalable and hardware-efficient deep learning
Chenghao Feng, Jiaqi Gu, Hanqing Zhu +7
The optical neural network (ONN) is a promising hardware platform for next-generation neuromorphic computing due to its high parallelism, low latency, and low energy consumption. H…
Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator
Hanqing Zhu, Jiaqi Gu, Hanrui Wang +7
The wide adoption and significant computing resource of attention-based transformers, e.g., Vision Transformers and large language models (LLM), have driven the demand for efficien…
M3ICRO: Machine Learning-Enabled Compact Photonic Tensor Core based on PRogrammable Multi-Operand Multimode Interference
Jiaqi Gu, Hanqing Zhu, Chenghao Feng +3
Photonic computing shows promise for transformative advancements in machine learning (ML) acceleration, offering ultra-fast speed, massive parallelism, and high energy efficiency.…
Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformers
Zixuan Jiang, Jiaqi Gu, Hanqing Zhu +1
Transformers have achieved great success in machine learning applications. Normalization techniques, such as Layer Normalization (LayerNorm, LN) and Root Mean Square Normalization…
Domain Wall-Magnetic Tunnel Junction Analog Content Addressable Memory Using Current and Projected Data
Harrison Jin, Hanqing Zhu, Keren Zhu +8
With the rise in in-memory computing architectures to reduce the compute-memory bottleneck, a new bottleneck is present between analog and digital conversion. Analog content-addres…
NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
Jiaqi Gu, Zhengqi Gao, Chenghao Feng +4
Optical computing is an emerging technology for next-generation efficient artificial intelligence (AI) due to its ultra-high speed and efficiency. Electromagnetic field simulation…
Fuse and Mix: MACAM-Enabled Analog Activation for Energy-Efficient Neural Acceleration
Hanqing Zhu, Keren Zhu, Jiaqi Gu +4
Analog computing has been recognized as a promising low-power alternative to digital counterparts for neural network acceleration. However, conventional analog computing is mainly…
Uni-Retriever: Towards Learning The Unified Embedding Based Retriever in Bing Sponsored Search
Jianjin Zhang, Zheng Liu, Weihao Han +9
Embedding based retrieval (EBR) is a fundamental building block in many web applications. However, EBR in sponsored search is distinguished from other generic scenarios and technic…
ELight: Enabling Efficient Photonic In-Memory Neurocomputing with Life Enhancement
Hanqing Zhu, Jiaqi Gu, Chenghao Feng +4
With the recent advances in optical phase change material (PCM), photonic in-memory neurocomputing has demonstrated its superiority in optical neural network (ONN) designs with nea…