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Convolution Properties II01:17

Convolution Properties II

448
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
448
Convolution Properties I01:20

Convolution Properties I

379
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
379
Parallel Processing01:20

Parallel Processing

444
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
444
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

673
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
673
Deconvolution01:20

Deconvolution

418
Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
418
Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
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統合フォトニックテンソールコアを使用した並列コンボリューション処理

J Feldmann1, N Youngblood2,3, M Karpov4

  • 1Institute of Physics, University of Münster, Münster, Germany.

Nature
|January 7, 2021
PubMed
まとめ
この要約は機械生成です。

光学ハードウェアの加速器である フォトニックテンソール・コアを開発し 1秒間に何兆もの 増量-蓄積操作を実行しました この統合されたフォトニックデバイスは データ密集型アプリケーションに より高速でスケーラブルな AI ハードウェアへの道を開きます

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科学分野:

  • 統合フォトニクス
  • 光学コンピューティング
  • 人工知能のハードウェア

背景:

  • モバイルネットワーク,IoT,AIからの指数関数的なデータ成長は より高速で効率的なハードウェアを必要とします
  • 大量のデータセットを処理するためのスピードとスケーラビリティの現在のハードウェアの制限.
  • コンピューティング密集したAIタスクのための特殊なハードウェアアクセラレータの必要性

研究 の 目的:

  • コンピューティングに特化した統合フォトニックハードウェア加速器 (テンサー・コア) を実証する.
  • フォトニック技術を用いた高速で並列化されたインメモリコンピューティングを実現する.
  • 未来の人工知能のハードウェアに 統合されたフォトニクスの可能性を 探求すること

主な方法:

  • フェーズチェンジマテリアルメモリ配列を用いた光子テンサーコアを開発した.
  • 計算のために光子チップベースの光学周波数コンブ (ソリトンマイクロコンブ) を使用しています.
  • 再構成可能なパッシブコンポーネントを介して光学伝送を測定する計算を減らす.

主要な成果:

  • 1秒間に何兆もの倍加-蓄積操作 (テラ-MACs/s) の動作速度を達成した.
  • 計算帯域幅が14ギガヘルツを超え,調節器と光検出器の速度によって制限されています.
  • フォトニック・テンソール・コアのCMOS・ウェーファー・スケール・インテグレーションへの道を示した.

結論:

  • 光学コンピューティングの 重要な進歩です
  • 統合フォトニクスは,並列,高速,効率的なAI計算のための有望なソリューションを提供します.
  • この技術は自動運転,ライブビデオ処理,クラウドコンピューティングに 応用できる可能性があります