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Found 14 papers, 3 papers with code
Unlocking Deep Learning: A BP-Free Approach for Parallel Block-Wise Training of Neural Networks
This includes decisions on how to decouple network blocks and which auxiliary networks to use for each block.
Leveraging Reinforcement Learning and Large Language Models for Code Optimization
We compare our framework with existing state-of-the-art models and show that it is more efficient with respect to speed and computational usage, as a result of the decrement in training steps and its applicability to models with fewer parameters.
Leader-Follower Neural Networks with Local Error Signals Inspired by Complex Collectives
Motivated by the intricacy of these collectives, we propose a neural network (NN) architecture inspired by the rules observed in nature's collective ensembles.
End-to-end Mapping in Heterogeneous Systems Using Graph Representation Learning
To enable heterogeneous computing systems with autonomous programming and optimization capabilities, we propose a unified, end-to-end, programmable graph representation learning (PGL) framework that is capable of mining the complexity of high-level programs down to the universal intermediate representation, extracting the specific computational patterns and predicting which code segments would run best on a specific core in heterogeneous hardware platforms.
Trust-aware Control for Intelligent Transportation Systems
Many intelligent transportation systems are multi-agent systems, i. e., both the traffic participants and the subsystems within the transportation infrastructure can be modeled as interacting agents.
VRoC: Variational Autoencoder-aided Multi-task Rumor Classifier Based on Text
VRoC consists of a co-train engine that trains variational autoencoders (VAEs) and rumor classification components.
A Vertex Cut based Framework for Load Balancing and Parallelism Optimization in Multi-core Systems
High-level applications, such as machine learning, are evolving from simple models based on multilayer perceptrons for simple image recognition to much deeper and more complex neural networks for self-driving vehicle control systems. The rapid increase in the consumption of memory and computational resources by these models demands the use of multi-core parallel systems to scale the execution of the complex emerging applications that depend on them.
Deep-PowerX: A Deep Learning-Based Framework for Low-Power Approximate Logic Synthesis
This paper aims at integrating three powerful techniques namely Deep Learning, Approximate Computing, and Low Power Design into a strategy to optimize logic at the synthesis level.
CSM-NN: Current Source Model Based Logic Circuit Simulation -- A Neural Network Approach
The miniaturization of transistors down to 5nm and beyond, plus the increasing complexity of integrated circuits, significantly aggravate short channel effects, and demand analysis and optimization of more design corners and modes.
NN-PARS: A Parallelized Neural Network Based Circuit Simulation Framework
The shrinking of transistor geometries as well as the increasing complexity of integrated circuits, significantly aggravate nonlinear design behavior.
Efficient Training of Deep Convolutional Neural Networks by Augmentation in Embedding Space
However, fine-tuning a transfer model with data augmentation in the raw input space has a high computational cost to run the full network for every augmented input.
LEX-GAN: Layered Explainable Rumor Detector Based on Generative Adversarial Networks
However, the vigorous growth of social media contributes to the fast-spreading and far-reaching rumors.
Approximate Logic Synthesis: A Reinforcement Learning-Based Technology Mapping Approach
Approximate Logic Synthesis (ALS) is the process of synthesizing and mapping a given Boolean network to a library of logic cells so that the magnitude/rate of error between outputs of the approximate and initial (exact) Boolean netlists is bounded from above by a predetermined total error threshold.
Hardware Architecture
High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis
Independent Component Analysis (ICA) is a dimensionality reduction technique that can boost efficiency of machine learning models that deal with probability density functions, e. g. Bayesian neural networks.
