• Sensors Online

    Recent understanding of our nervous system reveals that sensory organs are optimized for efficient uptake of information from the environment to the brain. While the bulk of the energy consumed in this process is to generate asynchronous action potentials for signal communication, evolutionary pressure has further optimized on this mode of communication within the brain for efficient representation and processing of information.

  • Linley Group

    Riding the AI-in-IoT wave, Eta Compute has developed an SoC that combines an MCU with a DSP for machine learning. The ultra-low-power Tensai chip is well suited to battery-operated systems, including those that require continuous (always-on) processing. The startup completes its solution with optimized neural-network software for machine learning. It expects Tensai to enter production early next year.

  • 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.