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Found 11 papers, 2 papers with code
Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework
Our 13B model (ChipGPT-FT) has a pass rate improvement compared with GPT-3. 5 in Verilog generation and outperforms in EDA script (i. e., SiliconCompiler) generation with only 200 EDA script data.
Adaptive Reconvergence-driven AIG Rewriting via Strategy Learning
Rewriting is a common procedure in logic synthesis aimed at improving the performance, power, and area (PPA) of circuits.
Cross-Layer Optimization for Fault-Tolerant Deep Learning
Fault-tolerant deep learning accelerator is the basis for highly reliable deep learning processing and critical to deploy deep learning in safety-critical applications such as avionics and robotics.
Fast Exact NPN Classification with Influence-aided Canonical Form
NPN classification has many applications in the synthesis and verification of digital circuits.
Exploring Winograd Convolution for Cost-effective Neural Network Fault Tolerance
When it is applied on fault-tolerant neural networks enhanced with fault-aware retraining and constrained activation functions, the resulting model accuracy generally shows significant improvement in presence of various faults.
MRFI: An Open Source Multi-Resolution Fault Injection Framework for Neural Network Processing
It enables users to modify an independent fault configuration file rather than neural network models for the fault injection and vulnerability analysis.
ChipGPT: How far are we from natural language hardware design
As large language models (LLMs) like ChatGPT exhibited unprecedented machine intelligence, it also shows great performance in assisting hardware engineers to realize higher-efficiency logic design via natural language interaction.
Gram-based Attentive Neural Ordinary Differential Equations Network for Video Nystagmography Classification
Video nystagmography (VNG) is the diagnostic gold standard of benign paroxysmal positional vertigo (BPPV), which requires medical professionals to examine the direction, frequency, intensity, duration, and variation in the strength of nystagmus on a VNG video.
Statistical Modeling of Soft Error Influence on Neural Networks
Prior work mainly rely on fault simulation to analyze the influence of soft errors on NN processing.
Fault-Tolerant Deep Learning: A Hierarchical Perspective
With the rapid advancements of deep learning in the past decade, it can be foreseen that deep learning will be continuously deployed in more and more safety-critical applications such as autonomous driving and robotics.
R2F: A Remote Retraining Framework for AIoT Processors with Computing Errors
It takes the remote AIoT processor with soft errors in the training loop such that the on-site computing errors can be learned with the application data on the server and the retrained models can be resilient to the soft errors.
