2 citations · 5 across the 7 of their papers we have counts for
7 papers
Automated Curvy Waveguide Routing for Large-Scale Photonic Integrated Circuits
Hongjian Zhou, Keren Zhu, Jiaqi Gu
As photonic integrated circuit (PIC) designs advance and grow in complexity, largely driven by innovations in photonic computing and interconnects, traditional manual physical desi…
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…
Practical Layout-Aware Analog/Mixed-Signal Design Automation with Bayesian Neural Networks
Ahmet F. Budak, Keren Zhu, David Z. Pan
The high simulation cost has been a bottleneck of practical analog/mixed-signal design automation. Many learning-based algorithms require thousands of simulated data points, which…
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…
TAG: Learning Circuit Spatial Embedding From Layouts
Keren Zhu, Hao Chen, Walker J. Turner +4
Analog and mixed-signal (AMS) circuit designs still rely on human design expertise. Machine learning has been assisting circuit design automation by replacing human experience with…
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…
Optimizer Fusion: Efficient Training with Better Locality and Parallelism
Zixuan Jiang, Jiaqi Gu, Mingjie Liu +2
Machine learning frameworks adopt iterative optimizers to train neural networks. Conventional eager execution separates the updating of trainable parameters from forward and backwa…