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Quantized Graph Neural Networks for Image Classification
Mathematics, 2023 Researchers have resorted to model quantization to compress and accelerate graph neural networks (GNNs). Nevertheless, several challenges remain: (1) quantization functions overlook outliers in the distribution, leading to increased quantization errors; (
Xinbiao Xu +3 more
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BMC Plant Biology, 2023 Background Rhamnus utilis Decne (Rhamnaceae) is an ecologically and economically important tree species. The growing market demands and recent anthropogenic impacts to R. utilis forests has negatively impacted its populations severely. However, little is
Song Guiquan +7 more
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How to Secure Valid Quantizations
Entropy, 2022 Canonical quantization has created many valid quantizations that require infinite-line coordinate variables. However, the half-harmonic oscillator, which is limited to the positive coordinate half, cannot receive a valid canonical quantization because of
John R. Klauder
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A dichotomy color quantization algorithm for the HSI color space
Scientific Reports, 2023 Color quantization is used to obtain an image with the same number of pixels as the original but represented using fewer colors. Most existing color quantization algorithms are based on the Red Green Blue (RGB) color space, and there are few color ...
Xia Yu +5 more
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Gradient Estimation for Ultra Low Precision POT and Additive POT Quantization
IEEE Access, 2023 Deep learning networks achieve high accuracy for many classification tasks in computer vision and natural language processing. As these models are usually over-parameterized, the computations and memory required are unsuitable for power-constrained ...
Huruy Tesfai +4 more
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Learning Bilateral Clipping Parametric Activation for Low-Bit Neural Networks
Mathematics, 2023 Among various network compression methods, network quantization has developed rapidly due to its superior compression performance. However, trivial activation quantization schemes limit the compression performance of network quantization.
Yunlong Ding, Di-Rong Chen
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Design Exploration of ReRAM-Based Crossbar for AI Inference
IEEE Access, 2021 ReRAM-based crossbar designs utilizing mixed-signal implementation has gained importance due to their low power, small size, low cost, and high throughput especially for multiply-and-add operations in AI-related applications.
Yasmin Halawani +2 more
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Quantization and Deployment of Deep Neural Networks on Microcontrollers
Sensors, 2021 Embedding Artificial Intelligence onto low-power devices is a challenging task that has been partly overcome with recent advances in machine learning and hardware design.
Pierre-Emmanuel Novac +4 more
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Training and Inference of Optical Neural Networks with Noise and Low-Bits Control
Applied Sciences, 2021 Optical neural networks (ONNs) are getting more and more attention due to their advantages such as high-speed and low power consumption. However, in a non-ideal environment, the noise and low-bits control may heavily lead to a decrease in the accuracy of
Danni Zhang +9 more
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An overview of the quantization for mixed distributions [PDF]
, 2017 The basic goal of quantization for probability distribution is to reduce the number of values, which is typically uncountable, describing a probability distribution to some finite set and thus approximation of a continuous probability distribution by a ...
Roychowdhury, Mrinal Kanti
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