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関連する概念動画

State Space Representation01:27

State Space Representation

519
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...
519
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

282
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
282
State Space to Transfer Function01:21

State Space to Transfer Function

552
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:
552
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

426
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
426
Transfer Function to State Space01:23

Transfer Function to State Space

748
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...
748
Linear time-invariant Systems01:23

Linear time-invariant Systems

863
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
863

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Updated: Jan 13, 2026

Author Spotlight: Unraveling Seizure Dynamics and Novel Therapeutics for Status Epilepticus Using CMOS High-Density Microelectrode Array Systems
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イベントシーケンス処理のためのステート空間モデルのインメモリコンピューティング実装

Xiaoyu Zhang1, Mingtao Hu1, Sen Lu1

  • 1Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA.

Nature communications
|January 9, 2026
PubMed
まとめ
この要約は機械生成です。

ステート空間モデル（SSM）は新しいハードウェアで効率的になりました。この研究では、AIタスクにおけるリアルタイム、イベント駆動型処理のために、SSMとコンピューティングインメモリハードウェアを統合します。

キーワード:
ステート空間モデルインメモリコンピューティングイベント駆動型処理AIハードウェア

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科学分野:

  • 人工知能
  • コンピュータ工学
  • 神経科学

背景:

  • ステート空間モデル（SSM）は、高度な長シーケンス処理機能を提供します。
  • SSMは、生物学的システムの機能を模倣して、リカレントネットワークと畳み込みネットワークを一般化します。
  • 既存のSSM実装は、エネルギー効率とリアルタイム処理の課題に直面しています。

研究 の 目的:

  • エネルギー効率の高いコンピューティングインメモリハードウェアにステート空間モデル（SSM）を実装すること。
  • AIアプリケーションのためのリアルタイム、イベント駆動型処理を達成すること。
  • パフォーマンス向上のためのアルゴリズムとハードウェアの協調設計を探求すること。

主な方法:

  • 実数値係数と共有減衰定数のための再パラメータ化されたSSM。
  • デバイスダイナミクスと対角化された状態遷移パラメータを活用しました。
  • 抵抗メモリを備えたクロスバーベースのコンピューティングインメモリシステムでネイティブに状態進化を実装しました。

主要な成果:

  • 提案システムを使用したAIタスクにおける高精度を達成しました。
  • 従来のメソッドと比較して大幅なエネルギー効率を実証しました。
  • イベントベースのビジョンとオーディオのための完全に非同期処理を可能にしました。

結論:

  • アルゴリズムとハードウェアの協調設計により、効率的なSSM実装が可能になります。
  • このシステムは、リアルタイム、低消費電力のAI処理への道を提供します。
  • このアプローチは、イベントベースの感覚データタスクに適しています。