Embedded Systems Engineering
November 27th, 2018 By Narayan Srinivasa, Eta Compute Neuromorphic Engineering results as data- and power-hungry conventional machine learning architectures shift from the cloud to power- and data-efficient machine learning in mobile and edge devices.
Embedded Computing Design
As IoT developers solve the connectivity, manageability, and security challenges of deploying devices at the edge, requirements now turn to making these systems smarter. Engineers are now tasked with integrating artificial intelligence (AI) into embedded systems at the far reaches of networks, which must minimize power consumption, communications latency, and cost while also becoming smarter.
Many startups set out with the goal of accomplishing a technical feat that was previously considered impossible. Quite frankly most do not succeed. Yet, occasionally a company comes along that succeeds with a game changing breakthrough. ETA Compute has done just this. Yet, even more impressively, this 3-year-old company has done more than just develop one “impossible” technological achievement, they have developed two.
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.
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.