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相关概念视频

Synthetic Biology02:55

Synthetic Biology

Synthetic biology is an interdisciplinary science that involves using principles from disciplines such as engineering, molecular biology, cell biology, and systems biology. It involves remodeling existing organisms from nature or constructing completely new synthetic organisms for applications such as protein or enzyme production, bioremediation, value-added macromolecule production, and the addition of desirable traits to crops, to name a few.
Golden rice
Golden rice is a genetically modified...
Assembly of Signaling Complexes01:30

Assembly of Signaling Complexes

Multiprotein signaling complexes are formed in a dynamic process involving protein-protein interactions at the cytoplasmic domain of transmembrane receptors or enzymatic and non-enzymatic proteins associated with the receptor. These complexes ensure the activation and propagation of intracellular signals that regulate cell functions.
Interaction domains in cell signaling
Interaction domains recognize exposed features of their binding partners containing post-translationally modified sequences,...
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Semiconductors01:22

Semiconductors

There is variation in the electrical conductivity of materials - metals, semiconductors, and insulators that are showcased with the help of the energy band diagrams.
Metals such as copper (Cu), zinc (Zn), or lead (Pb) have low resistivity and feature conduction bands that are either not fully occupied or overlap with the valence band, making a bandgap non-existent. This allows electrons in the highest energy levels of the valence band to easily transition to the conduction band upon gaining...
Parallel Processing01:20

Parallel Processing

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...
Synthetic Disvision of Polynomials01:28

Synthetic Disvision of Polynomials

Synthetic division is an efficient algorithmic approach for dividing a polynomial by a linear binomial of the form x - c, where c is a real number. This method is helpful due to its streamlined process, which avoids the more cumbersome steps involved in the traditional long division of polynomials. It simplifies computation and serves as a practical tool for evaluating polynomials and identifying their factors.To perform synthetic division, one begins by listing the coefficients of the...

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相关实验视频

Updated: Jun 17, 2026

Scalable Fluidic Injector Arrays for Viral Targeting of Intact 3-D Brain Circuits
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tinyHLS:一个新的开源高层合成工具,针对人工神经网络推断的硬件加速器.

Ingo Hoyer1, Alexander Utz1, Christoph Hoog Antink2

  • 1Fraunhofer Institute for Microelectronic Circuits and Systems IMS, Duisburg, Germany.

Physiological measurement
|January 10, 2025
PubMed
概括

一个新的Python包,tinyHLS,通过将AI模型转换为硬件加速器,使可穿戴设备上的实时AI分析成为可能. 这大大提高了处理速度和能源效率,用于像ECG监控这样的应用.

关键词:
在这里,我们可以看到AIAIAI.在 HLS HLS 中.这就是RISC-V的风险.生物信号 生物信号

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科学领域:

  • 人工智能的人工智能
  • 硬件加速器 硬件加速器
  • 可穿戴技术可穿戴技术

背景情况:

  • 像智能手表这样的可穿戴设备在生物信号监测 (例如,心电图) 中表现出色,但缺乏实时内置分析的能力.
  • 有限的电源限制了边缘设备对复杂AI任务的计算能力.

研究的目的:

  • 介绍tinyHLS,一个新的Python包,用于将AI模型转换为硬件加速器.
  • 直接在资源有限的可穿戴设备上实现高效的实时数据分析.
  • 促进人工智能融入硬件加速器的开发工作流程.

主要方法:

  • 开发了 tinyHLS,这是一个Python包,可以将AI模型 (TensorFlow Keras) 翻译成硬件描述语言代码.
  • 使用基于模板的硬件编译器,支持各种神经网络层和激活功能.
  • 专注于1D数据,特别是与生物信号处理相关的时间序列数据.

主要成果:

  • 生成的硬件加速器显示处理速度增加了62倍,能源效率提高了4.5倍.
  • 通过ECG数据检测心房动的验证加速器.
  • 使用商业ASIC设计工具确保代码质量和可合成性.

结论:

  • tinyHLS提供了一个开源的多功能解决方案,用于开发边缘设备上AI的硬件加速器.
  • 该包支持无集成到现有的AI开发工作流程中.
  • 潜在的应用包括边缘计算,云服务和增强可穿戴生物信号分析.