1,133 research outputs found
Low-bit Shift Network for End-to-End Spoken Language Understanding
Deep neural networks (DNN) have achieved impressive success in multiple
domains. Over the years, the accuracy of these models has increased with the
proliferation of deeper and more complex architectures. Thus, state-of-the-art
solutions are often computationally expensive, which makes them unfit to be
deployed on edge computing platforms. In order to mitigate the high
computation, memory, and power requirements of inferring convolutional neural
networks (CNNs), we propose the use of power-of-two quantization, which
quantizes continuous parameters into low-bit power-of-two values. This reduces
computational complexity by removing expensive multiplication operations and
with the use of low-bit weights. ResNet is adopted as the building block of our
solution and the proposed model is evaluated on a spoken language understanding
(SLU) task. Experimental results show improved performance for shift neural
network architectures, with our low-bit quantization achieving 98.76 \% on the
test set which is comparable performance to its full-precision counterpart and
state-of-the-art solutions.Comment: Accepted at INTERSPEECH 202
Traffic Scheduling Strategy of Power Communication Network Based on SDN
Due to the complicated structure, power communication network is difficult to guarantee the quality of service (QoS) of power services. A two-level scheduling algorithm based on software defined network (SDN) is proposed in this paper. Firstly, the priority-based scheduling method is used to meet the latency-sensitive of power service. Then, in order to alleviate congestion, queue bandwidth is adjusted according to network state information, which can be collected by the centralized control of SDN. Finally, the Mininet and Ryu controller are made use of building simulation environment. The test results show that the algorithm proposed in this paper reduce delay and packet loss rate significantly, which achieves QoS
DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization
Efficiently deploying deep neural networks on low-resource edge devices is
challenging due to their ever-increasing resource requirements. To address this
issue, researchers have proposed multiplication-free neural networks, such as
Power-of-Two quantization, or also known as Shift networks, which aim to reduce
memory usage and simplify computation. However, existing low-bit Shift networks
are not as accurate as their full-precision counterparts, typically suffering
from limited weight range encoding schemes and quantization loss. In this
paper, we propose the DenseShift network, which significantly improves the
accuracy of Shift networks, achieving competitive performance to full-precision
networks for vision and speech applications. In addition, we introduce a method
to deploy an efficient DenseShift network using non-quantized floating-point
activations, while obtaining 1.6X speed-up over existing methods. To achieve
this, we demonstrate that zero-weight values in low-bit Shift networks do not
contribute to model capacity and negatively impact inference computation. To
address this issue, we propose a zero-free shifting mechanism that simplifies
inference and increases model capacity. We further propose a sign-scale
decomposition design to enhance training efficiency and a low-variance random
initialization strategy to improve the model's transfer learning performance.
Our extensive experiments on various computer vision and speech tasks
demonstrate that DenseShift outperforms existing low-bit multiplication-free
networks and achieves competitive performance compared to full-precision
networks. Furthermore, our proposed approach exhibits strong transfer learning
performance without a drop in accuracy. Our code was released on GitHub
Extracting the Quantum Geometric Tensor of an Optical Raman Lattice by Bloch State Tomography
In Hilbert space, the geometry of the quantum state is identified by the
quantum geometric tensor (QGT), whose imaginary part is the Berry curvature and
real part is the quantum metric tensor. Here, we propose and experimentally
implement a complete Bloch state tomography to directly measure eigenfunction
of an optical Raman lattice for ultracold atoms. Through the measured
eigenfunction, the distribution of the complete QGT in the Brillouin zone is
reconstructed, with which the topological invariants are extracted by the Berry
curvature and the distances of quantum states in momentum space are measured by
the quantum metric tensor. Further, we experimentally test a predicted
inequality between the Berry curvature and quantum metric tensor, which reveals
a deep connection between topology and geometry
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