fastiSSM:快速推断状态空间模型与频率域在线模型近似
Jiaxuan Chen1, Yancheng Xie1, Hai Wang1
1School of Integrated Circuit Science and Engineering, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.
概括
fastiSSM加速状态空间模型 (SSM) 用于长序时间序列预测和NLP任务. 该方法使用在线模型近似进行高效准确的推断,克服速度和内存限制.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 国家空间模型 (SSM) 显示了长序时间序列预测 (LSTF) 和自然语言处理 (NLP) 的前景.
- 大型SSM的推断速度较慢,内存消耗较高,限制了它们在资源有限的环境中使用.
- 对于SSM,现有的模型近似技术在计算效率和准确性方面面临挑战.
研究的目的:
- 提出 fastiSSM,一种加速推断一般状态空间模型 (SSM) 的新方法.
- 解决当前SSM近似方法的计算效率和近似精度限制.
- 为了使SSM在具有严格速度,内存和能源限制的应用中部署.
主要方法:
- 在连续时间模型中引入了参数共享结构,以实现高效的近似.
- 开发了一种基于频域的模型近似技术,利用SSM的线性时间不变 (LTI) 属性.
- 实现在线模型近似,用于生成紧的复发,以便快速推断.
主要成果:
- fastiSSM实现了高模型近似效率,因为它只需要一个近似对紧的复发.
- 频域方法产生了强大的紧型模型,并改进了错误控制.
- 实验结果表明,fastiSSM能够有效地加快SSM推断,在LSTF和音频生成任务上的边际误差.
结论:
- fastiSSM显著提高了大规模SSM的推断速度和效率.
- 该方法在模型近似效率和推理性能方面都优于现有技术.
- fastiSSM提供了一个实用的解决方案,用于在速度/内存/能量受限制的应用中部署SSM,并提供开源代码.
相关概念视频
State Space Representation
492
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Consider an RLC circuit, a...
492
Linear Approximation in Time Domain
310
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
310
Linear Approximation in Frequency Domain
328
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
328
State Space to Transfer Function
530
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
530
Transfer Function to State Space
725
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an RLC...
In an RLC...
725
Aliasing
519
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
519


