• Embedded Computing

    The world is moving toward a smart and distributed computing model of interacting devices. Intelligence in these devices will be driven by machine learning algorithms. Yet, extending machine learning to the edge is not without its challenges. This paper will discuss the landscape of these challenges and then describe how neuromorphic – brain-inspired – computing will enable a wide range of intelligent applications that address these challenges. Examples of this technology including handwriting recognition and continuous speech recognition are provided.

  • IEEE Spectrum

    Chip can learn on its own and inference at 100-microwatt scale, says company at Arm TechCon. Eta Compute’s third-generation chip, called TENSAI, also does traditional deep learning using convolutional neural networks. Potential customers already have samples of the new chip, and the company expects to begin mass production in the first quarter of 2019.

  • Embedded Systems Engineering

    Why microcontrollers architected in a “brain inspired” way could have a growing role in smart metering and other point-to-point communication applications, such as those relying on sub-GHz next generation networks.

  • Embedded Systems Engineering

    Why microcontrollers architected in a “brain inspired” way could have a growing role in smart metering and other point-to-point communication applications, such as those relying on sub-GHz next generation networks.

  • Electronic Design

    From self-driving cars to the industrial Internet of Things, neural networks are reshaping the problem-solving methods of developers. William Wong | Jun 29, 2018