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Found 8 papers, 2 papers with code
Radial Networks: Dynamic Layer Routing for High-Performance Large Language Models
In this work, we explore the practicality of layer sparsity by profiling residual connections and establish the relationship between model depth and layer sparsity.
Exploring the Limits of Semantic Image Compression at Micro-bits per Pixel
Traditional methods, such as JPEG, perform image compression by operating on structural information, such as pixel values or frequency content.
FLIQS: One-Shot Mixed-Precision Floating-Point and Integer Quantization Search
With the proposed integer quantization search, we increase the accuracy of ResNet-18 on ImageNet by 1. 31% points and ResNet-50 by 0. 90% points with equivalent model cost over previous methods.
Enabling Design Methodologies and Future Trends for Edge AI: Specialization and Co-design
Artificial intelligence (AI) technologies have dramatically advanced in recent years, resulting in revolutionary changes in people's lives.
Logic Synthesis Meets Machine Learning: Trading Exactness for Generalization
If the function is incompletely-specified, the implementation has to be true only on the care set.
OverQ: Opportunistic Outlier Quantization for Neural Network Accelerators
Specialized hardware for handling activation outliers can enable low-precision neural networks, but at the cost of nontrivial area overhead.
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
The majority of existing literature focuses on training quantized DNNs, while this work examines the less-studied topic of quantizing a floating-point model without (re)training.
Building Efficient Deep Neural Networks with Unitary Group Convolutions
UGConvs generalize two disparate ideas in CNN architecture, channel shuffling (i. e. ShuffleNet) and block-circulant networks (i. e. CirCNN), and provide unifying insights that lead to a deeper understanding of each technique.
