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相关概念视频

Neural Circuits01:25

Neural Circuits

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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...
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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量子化意识的NN层与高通量FPGA实现边缘AI.

Mara Pistellato1, Filippo Bergamasco1, Gianluca Bigaglia2

  • 1Dipartimento di Scienze Ambientali, Informatica e Statistica (DAIS), Università Ca'Foscari di Venezia, Via Torino 155, 30170 Venezia, Italy.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括
此摘要是机器生成的。

本研究介绍了针对现场可编程网关数组 (FPGA) 的自定义深度学习层,从而实现高效的实时工业推理. 这种新的方法在低位精度下实现了高精度,优于传统方法.

关键词:
在FPGA中,FPGA是指FPGA.边缘人工智能 边缘人工智能峰值检测可以检测到峰值.量子化意识培训的培训量化了CNN和CNN的情况.

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科学领域:

  • 深度学习和人工智能
  • 硬件加速用于机器学习.
  • 嵌入式系统和实时计算实时计算

背景情况:

  • 深度学习,特别是卷积神经网络 (CNN),在各种应用中提供了显著的优势.
  • 消费者个人电脑 (PC) 硬件通常不适合工业环境,因为恶劣的条件和严格的时间要求.
  • 现场可编程网关数组 (FPGA) 正在在工业环境中获得高效网络推断的吸引力.

研究的目的:

  • 提出一种新的定制网络架构家族,用于对FPGA进行实时推理.
  • 开发一个可训练的量化层 (Requantizer) 整数算术与可定制的精度 (到两个位).
  • 为了使图形处理单元 (GPU) 的高效培训和随后的合成到FPGA硬件.

主要方法:

  • 设计定制层,使用整数算术,可调节的比特精度.
  • 开发了一个可训练的量子化层",Requantizer",用于非线性激活和值重新缩放.
  • 在GPU上使用TensorFlow Lite训练模型,并使用Vivado对Xilinx FPGAs进行合成.

主要成果:

  • 实现了与浮点版本相比的量子化网络准确性,而不需要校准数据.
  • 在一个案例研究中,与专用峰值检测算法相比,演示了优越的性能.
  • 实现FPGA实现了以每秒4千兆像素的实时处理,效率为0.5TOPS/W.

结论:

  • 拟议的定制FPGA解决方案为工业应用中的实时深度学习推理提供了可行的替代方案.
  • 量子化意识的培训方法有效地处理有限的精度约束.
  • 开发的硬件加速器提供了高性能和能源效率,与定制集成解决方案相竞争.