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Zariski Quantization as Second Quantization [PDF]
The Zariski quantization is one of the strong candidates for a quantization of the Nambu-Poisson bracket. In this paper, we apply the Zariski quantization for first quantized field theories, such as superstring and supermembrane theories, and clarify ...
Sato, Matsuo
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Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural Network Inference
Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data throughput and reduced energy consumption.
Benjamin Hawks, J Duarte
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Deep Neural Network Compression With Single and Multiple Level Quantization [PDF]
Network quantization is an effective solution to compress deep neural networks for practical usage. Existing network quantization methods cannot sufficiently exploit the depth information to generate low-bit compressed network. In this paper, we propose two novel network quantization approaches, single-level network quantization (SLQ) for high-bit ...
exaly +1 more source
Quantized Graph Neural Networks for Image Classification
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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How to Secure Valid Quantizations
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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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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A dichotomy color quantization algorithm for the HSI color space
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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ConvAix: An Application-Specific Instruction-Set Processor for the Efficient Acceleration of CNNs
ConvAix is an application-specific instruction-set processor (ASIP) that enables the energy-efficient processing of convolutional neural networks (CNNs) while retaining substantial flexibility through its instruction-set architecture (ISA) based design ...
Andreas Bytyn+2 more
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Robust 2-bit Quantization of Weights in Neural Network Modeled by Laplacian Distribution
Significant efforts are constantly involved in finding manners to decrease the number of bits required for quantization of neural network parameters. Although in addition to compression, in neural networks, the application of quantizer models that are ...
PERIC, Z.+3 more
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Data reduction through optimized scalar quantization for more compact neural networks
Raw data generation for several existing and planned large physics experiments now exceeds TB/s rates, generating untenable data sets in very little time. Those data often demonstrate high dimensionality while containing limited information.
BerthiƩ Gouin-Ferland+2 more
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