30 citations · 74 across the 13 of their papers we have counts for
13 papers
When Forgetting Builds Reliability: LLM Unlearning for Reliable Hardware Code Generation
Yiwen Liang, Qiufeng Li, Shikai Wang +1
Large Language Models (LLMs) have shown strong potential in accelerating digital hardware design through automated code generation. Yet, ensuring their reliability remains a critic…
HeaRT: A Hierarchical Circuit Reasoning Tree-Based Agentic Framework for AMS Design Optimization
Souradip Poddar, Chia-Tung Ho, Ziming Wei +3
Conventional AI-driven AMS design automation algorithms remain constrained by their reliance on high-quality datasets to capture underlying circuit behavior, coupled with poor tran…
Unicorn-CIM: Uncovering the Vulnerability and Improving the Resilience of High-Precision Compute-in-Memory
Qiufeng Li, Yiwen Liang, Weidong Cao
Compute-in-memory (CIM) architecture has been widely explored to address the von Neumann bottleneck in accelerating deep neural networks (DNNs). However, its reliability remains la…
AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies
Jian Gao, Weidong Cao, Junyi Yang +1
The massive and large-scale design of foundational semiconductor integrated circuits (ICs) is crucial to sustaining the advancement of many emerging and future technologies, such a…
A Hybrid-Domain Floating-Point Compute-in-Memory Architecture for Efficient Acceleration of High-Precision Deep Neural Networks
Zhiqiang Yi, Yiwen Liang, Weidong Cao
Compute-in-memory (CIM) has shown significant potential in efficiently accelerating deep neural networks (DNNs) at the edge, particularly in speeding up quantized models for infere…
RoSE-Opt: Robust and Efficient Analog Circuit Parameter Optimization with Knowledge-infused Reinforcement Learning
Weidong Cao, Jian Gao, Tianrui Ma +3
This paper proposes a learning framework, RoSE-Opt, to achieve robust and efficient analog circuit parameter optimization. RoSE-Opt has two important features. First, it incorporat…
CktGNN: Circuit Graph Neural Network for Electronic Design Automation
Zehao Dong, Weidong Cao, Muhan Zhang +3
The electronic design automation of analog circuits has been a longstanding challenge in the integrated circuit field due to the huge design space and complex design trade-offs amo…
Non-Hermitian Physics-Inspired Voltage-Controlled Oscillators with Resistive Tuning
Weidong Cao, Hua Wang, Xuan Zhang
This paper presents a non-Hermitian physics-inspired voltage-controlled oscillator (VCO) topology, which is termed parity-time-symmetric topology. The VCO consists of two coupled i…
Domain Knowledge-Infused Deep Learning for Automated Analog/Radio-Frequency Circuit Parameter Optimization
Weidong Cao, Mouhacine Benosman, Xuan Zhang +1
The design automation of analog circuits is a longstanding challenge. This paper presents a reinforcement learning method enhanced by graph learning to automate the analog circuit…
Demonstration of fully integrated parity-time-symmetric electronics
Weidong Cao, Changqing Wang, Weijian Chen +4
Harnessing parity-time (PT) symmetry with balanced gain and loss profiles has created a variety of opportunities in electronics from wireless energy transfer to telemetry sensing a…
Domain Knowledge-Based Automated Analog Circuit Design with Deep Reinforcement Learning
Weidong Cao, Mouhacine Benosman, Xuan Zhang +1
The design automation of analog circuits is a longstanding challenge in the integrated circuit field. This paper presents a deep reinforcement learning method to expedite the desig…
Neural-PIM: Efficient Processing-In-Memory with Neural Approximation of Peripherals
Weidong Cao, Yilong Zhao, Adith Boloor +3
Processing-in-memory (PIM) architectures have demonstrated great potential in accelerating numerous deep learning tasks. Particularly, resistive random-access memory (RRAM) devices…
Neural Network-Inspired Analog-to-Digital Conversion to Achieve Super-Resolution with Low-Precision RRAM Devices
Weidong Cao, Liu Ke, Ayan Chakrabarti +1
Recent works propose neural network- (NN-) inspired analog-to-digital converters (NNADCs) and demonstrate their great potentials in many emerging applications. These NNADCs often r…