28 citations · 38 across the 18 of their papers we have counts for
18 papers
Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling
Jiaqi Yin, Cunxi Yu
Scheduling on dataflow graphs (also known as computation graphs) is an NP-hard problem. The traditional exact methods are limited by runtime complexity, while reinforcement learnin…
Gamora: Graph Learning based Symbolic Reasoning for Large-Scale Boolean Networks
Nan Wu, Yingjie Li, Cong Hao +3
Reasoning high-level abstractions from bit-blasted Boolean networks (BNs) such as gate-level netlists can significantly benefit functional verification, logic minimization, datapat…
Device-system Co-design of Photonic Neuromorphic Processor using Reinforcement Learning
Yingheng Tang, Princess Tara Zamani, Ruiyang Chen +4
The incorporation of high-performance optoelectronic devices into photonic neuromorphic processors can substantially accelerate computationally intensive operations in machine lear…
Physics-aware Complex-valued Adversarial Machine Learning in Reconfigurable Diffractive All-optical Neural Network
Ruiyang Chen, Yingjie Li, Minhan Lou +5
Diffractive optical neural networks have shown promising advantages over electronic circuits for accelerating modern machine learning (ML) algorithms. However, it is challenging to…
FlowTune: End-to-end Automatic Logic Optimization Exploration via Domain-specific Multi-armed Bandit
Walter Lau Neto, Yingjie Li, Pierre-Emmanuel Gaillardon +1
Recent years have seen increasing employment of decision intelligence in electronic design automation (EDA), which aims to reduce the manual efforts and boost the design closure pr…
Physics-Guided and Physics-Explainable Recurrent Neural Network for Time Dynamics in Optical Resonances
Yingheng Tang, Jichao Fan, Xinwei Li +4
Understanding the time evolution of physical systems is crucial to revealing fundamental characteristics that are hidden in frequency domain. In optical science, high-quality reson…
Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design
Yingjie Li, Ruiyang Chen, Berardi Sensale Rodriguez +2
Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments. Recently, there are increasing…
Artificial Intelligence Accelerators based on Graphene Optoelectronic Devices
Weilu Gao, Cunxi Yu, Ruiyang Chen
Optical and optoelectronic approaches of performing matrix-vector multiplication (MVM) operations have shown the great promise of accelerating machine learning (ML) algorithms with…
Contrastive Weight Regularization for Large Minibatch SGD
Qiwei Yuan, Weizhe Hua, Yi Zhou +1
The minibatch stochastic gradient descent method (SGD) is widely applied in deep learning due to its efficiency and scalability that enable training deep networks with a large volu…
Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets
Cunxi Yu, Zhiru Zhang
Physical design process commonly consumes hours to days for large designs, and routing is known as the most critical step. Demands for accurate routing quality prediction raise to…
Spectral Approach to Verifying Non-linear Arithmetic Circuits
Cunxi Yu, Tiankai Su, Atif Yasin +1
This paper presents a fast and effective computer algebraic method for analyzing and verifying non-linear integer arithmetic circuits using a novel algebraic spectral model. It int…
Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning
Cunxi Yu, Wang Zhou
Due to the increasing complexity of Integrated Circuits (ICs) and System-on-Chip (SoC), developing high-quality synthesis flows within a short market time becomes more challenging.…
Algorithmic Obfuscation over GF()
Cunxi Yu, Daniel Holcomb
Galois Field arithmetic blocks are the key components in many security applications, such as Elliptic Curve Cryptography (ECC) and the S-Boxes of the Advanced Encryption Standard (…
Developing Synthesis Flows Without Human Knowledge
Cunxi Yu, Houping Xiao, Giovanni De Micheli
Design flows are the explicit combinations of design transformations, primarily involved in synthesis, placement and routing processes, to accomplish the design of Integrated Circu…
Formal Analysis of Galois Field Arithmetics - Parallel Verification and Reverse Engineering
Cunxi Yu, Maciej Ciesielski
Galois field (GF) arithmetic circuits find numerous applications in communications, signal processing, and security engineering. Formal verification techniques of GF circuits are s…
Advanced Datapath Synthesis using Graph Isomorphism
Cunxi Yu, Mihir Choudhury, Andrew Sullivan +1
This paper presents an advanced DAG-based algorithm for datapath synthesis that targets area minimization using logic-level resource sharing. The problem of identifying common spec…
Reverse Engineering of Irreducible Polynomials in GF(2^m) Arithmetic
Cunxi Yu, Daniel Holcomb, Maciej Ciesielski
Current techniques for formally verifying circuits implemented in Galois field (GF) arithmetic are limited to those with a known irreducible polynomial P(x). This paper presents a…
Efficient Parallel Verification of Galois Field Multipliers
Cunxi Yu, Maciej Ciesielski
Galois field (GF) arithmetic is used to implement critical arithmetic components in communication and security-related hardware, and verification of such components is of prime imp…