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Found 16 papers, 4 papers with code
Less is More: Hop-Wise Graph Attention for Scalable and Generalizable Learning on Circuits
In this work we propose HOGA, a novel attention-based model for learning circuit representations in a scalable and generalizable manner.
BoolGebra: Attributed Graph-learning for Boolean Algebraic Manipulation
Boolean algebraic manipulation is at the core of logic synthesis in Electronic Design Automation (EDA) design flow.
Verilog-to-PyG -- A Framework for Graph Learning and Augmentation on RTL Designs
The complexity of modern hardware designs necessitates advanced methodologies for optimizing and analyzing modern digital systems.
Accelerating Exact Combinatorial Optimization via RL-based Initialization -- A Case Study in Scheduling
The traditional exact methods are limited by runtime complexity, while reinforcement learning (RL) and heuristic-based approaches struggle with determinism and solution quality.
Rubik's Optical Neural Networks: Multi-task Learning with Physics-aware Rotation Architecture
Recently, there are increasing efforts on advancing optical neural networks (ONNs), which bring significant advantages for machine learning (ML) in terms of power efficiency, parallelism, and computational speed.
RESPECT: Reinforcement Learning based Edge Scheduling on Pipelined Coral Edge TPUs
Deep neural networks (DNNs) have substantial computational and memory requirements, and the compilation of its computational graphs has a great impact on the performance of resource-constrained (e. g., computation, I/O, and memory-bound) edge computing systems.
Physics-aware Roughness Optimization for Diffractive Optical Neural Networks
As a representative next-generation device/circuit technology beyond CMOS, diffractive optical neural networks (DONNs) have shown promising advantages over conventional deep neural networks due to extreme fast computation speed (light speed) and low energy consumption.
Physics-aware Differentiable Discrete Codesign for Diffractive Optical Neural Networks
Diffractive optical neural networks (DONNs) have attracted lots of attention as they bring significant advantages in terms of power efficiency, parallelism, and computational speed compared with conventional deep neural networks (DNNs), which have intrinsic limitations when implemented on digital platforms.
Differentiable Discrete Device-to-System Codesign for Optical Neural Networks via Gumbel-Softmax
Specifically, Gumbel-Softmax with a novel complex-domain regularization method is employed to enable differentiable one-to-one mapping from discrete device parameters into the forward function of DONNs, where the physical parameters in DONNs can be trained by simply minimizing the loss function of the ML task.
Combinatorial Reinforcement Learning Based Scheduling for DNN Execution on Edge
The past half-decade has seen unprecedented growth in machine learning with deep neural networks (DNNs) that represent state-of-the-art in many real-world applications.
Real-time Multi-Task Diffractive Deep Neural Networks via Hardware-Software Co-design
Deep neural networks (DNNs) have substantial computational requirements, which greatly limit their performance in resource-constrained environments.
Contrastive Weight Regularization for Large Minibatch SGD
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 volume of data.
Painting on Placement: Forecasting Routing Congestion using Conditional Generative Adversarial Nets
Physical design process commonly consumes hours to days for large designs, and routing is known as the most critical step.
Performance Estimation of Synthesis Flows cross Technologies using LSTMs and Transfer Learning
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($2^m$)
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 (AES) cipher.
Cryptography and Security
Developing Synthesis Flows Without Human Knowledge
Design flows are the explicit combinations of design transformations, primarily involved in synthesis, placement and routing processes, to accomplish the design of Integrated Circuits (ICs) and System-on-Chip (SoC).
