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Found 27 papers, 4 papers with code
CAMO: Correlation-Aware Mask Optimization with Modulated Reinforcement Learning
Optical proximity correction (OPC) is a vital step to ensure printability in modern VLSI manufacturing.
RL-MUL: Multiplier Design Optimization with Deep Reinforcement Learning
The agent can learn to optimize the multiplier structure based on a Pareto-driven reward which is customized to accommodate the trade-off between area and delay.
BadRL: Sparse Targeted Backdoor Attack Against Reinforcement Learning
Our algorithm, BadRL, strategically chooses state observations with high attack values to inject triggers during training and testing, thereby reducing the chances of detection.
DevelSet: Deep Neural Level Set for Instant Mask Optimization
To further enhance printability and fast iterative convergence, we propose a novel deep neural network delicately designed with level set intrinsic principles to facilitate the joint optimization of DNN and GPU accelerated level set optimizer.
AdaOPC: A Self-Adaptive Mask Optimization Framework For Real Design Patterns
We inspect the pattern distribution on a design layer and find that different sub-regions have different pattern complexity.
Physics-Informed Optical Kernel Regression Using Complex-valued Neural Fields
Lithography is fundamental to integrated circuit fabrication, necessitating large computation overhead.
Adversarial Attacks on Adversarial Bandits
We also derived a theoretical lower bound on the cumulative attack cost that any victim-agnostic attack algorithm must incur.
Rethinking Graph Neural Networks for the Graph Coloring Problem
Graph coloring, a classical and critical NP-hard problem, is the problem of assigning connected nodes as different colors as possible.
Game Redesign in No-regret Game Playing
We study the game redesign problem in which an external designer has the ability to change the payoff function in each round, but incurs a design cost for deviating from the original game.
Machine Learning for Electronic Design Automation: A Survey
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing.
Rethinking Graph Neural Networks for Graph Coloring
To characterize the power of GNNs for the graph coloring problem, we first formalize the discrimination power of GNNs as the capability to assign nodes different colors.
Sequential Attacks on Kalman Filter-based Forward Collision Warning Systems
In this paper, we study adversarial attacks on KF as part of the more complex machine-human hybrid system of Forward Collision Warning.
Using Machine Teaching to Investigate Human Assumptions when Teaching Reinforcement Learners
To solve the machine teaching optimization problem, we use a deep learning approximation method which simulates learners in the environment and learns to predict how feedback affects the learner's internal states.
Task-agnostic Exploration in Reinforcement Learning
To address these challenges, we propose the \textit{task-agnostic RL} framework: In the exploration phase, the agent first collects trajectories by exploring the MDP without the guidance of a reward function.
The Sample Complexity of Teaching-by-Reinforcement on Q-Learning
Our TDim results provide the minimum number of samples needed for reinforcement learning, and we discuss their connections to standard PAC-style RL sample complexity and teaching-by-demonstration sample complexity results.
Adaptive Reward-Poisoning Attacks against Reinforcement Learning
In reward-poisoning attacks against reinforcement learning (RL), an attacker can perturb the environment reward $r_t$ into $r_t+\delta_t$ at each step, with the goal of forcing the RL agent to learn a nefarious policy.
VLSI Mask Optimization: From Shallow To Deep Learning
VLSI mask optimization is one of the most critical stages in manufacturability aware design, which is costly due to the complicated mask optimization and lithography simulation.
Policy Poisoning in Batch Reinforcement Learning and Control
We study a security threat to batch reinforcement learning and control where the attacker aims to poison the learned policy.
Are Adversarial Perturbations a Showstopper for ML-Based CAD? A Case Study on CNN-Based Lithographic Hotspot Detection
There is substantial interest in the use of machine learning (ML) based techniques throughout the electronic computer-aided design (CAD) flow, particularly those based on deep learning.
Collaborative and Privacy-Preserving Machine Teaching via Consensus Optimization
To address these challenges, we formulate collaborative teaching as a consensus and privacy-preserving optimization process to minimize teaching risk.
Data Poisoning against Differentially-Private Learners: Attacks and Defenses
Data poisoning attacks aim to manipulate the model produced by a learning algorithm by adversarially modifying the training set.
Adversarial Attacks on Stochastic Bandits
We study adversarial attacks that manipulate the reward signals to control the actions chosen by a stochastic multi-armed bandit algorithm.
Data Poisoning Attacks in Contextual Bandits
We provide a general attack framework based on convex optimization and show that by slightly manipulating rewards in the data, an attacker can force the bandit algorithm to pull a target arm for a target contextual vector.
A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks
To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference.
Recent Advances in Convolutional Neural Network Acceleration
We propose a taxonomy in terms of three levels, i. e.~structure level, algorithm level, and implementation level, for acceleration methods.
Cross-layer Optimization for High Speed Adders: A Pareto Driven Machine Learning Approach
In spite of maturity to the modern electronic design automation (EDA) tools, optimized designs at architectural stage may become sub-optimal after going through physical design flow.
Teacher Improves Learning by Selecting a Training Subset
We call a learner super-teachable if a teacher can trim down an iid training set while making the learner learn even better.
