"Some people have a way with words, and other people, uh, not have way." The scariest part of moving to a new country is learning a new language. You step off the plane and into a new land with illegible signs, strange customs, and unfamiliar culture. Whom do I ask for help? Where do I even start?

Source: Eta Compute's Tensai Flow Puts Machine Learning at the Edge of the IoT

Eta Compute, a machine learning company, recently announced its new TENSAI Flow software, which is designed to complement the company's existing development resources and enable design from concept to firmware in IoT and low power edge devices. "Neural network and embedded software designers are seeking practical ways to make developing machine learning for edge applications less frustrating and time-consuming," said Ted Tewksbury, CEO, Eta Compute. Now, designers can optimize neural networks by reducing memory size, the number of operations, and power consumption, and embedded software designers can reduce the complexities of adding AI to embedded edge devices, saving months of development time." "In order to best unlock the benefits of TinyML we need highly optimized hardware and algorithms. Eta Compute's TENSAI provides an ideal combination of highly efficient ML hardware, coupled with an optimized neural network compiler," said Zach Shelby, CEO, Edge Impulse. "Together with Edge Impulse and the TENSAI Sensor Board this is the best possible solution to achieve extremely low-power ML applications." It includes a neural network compiler, a neural network zoo, and middleware comprising FreeRTOS, HAL and frameworks for sensors, as well as IoT/cloud enablement. "Google and the TensorFlow team have been dedicated in bringing machine learning with the tiniest devices.

Eta Compute has developed a high-efficiency ASIC and new artificial intelligence (AI) software based on neural networks to solve the problems of edge and mobile devices without the use of cloud resources. Future mobile devices, which are constantly active in the IoT ecosystem, require a disruptive solution that offers processing power to enable machine intelligence with low power consumption for applications such as speech recognition and imaging. These are the types of applications for which Eta Compute designed its ECM3531. The IC is based on the ARM Cortex-M3 and NXP Coolflux DSP processors. It uses a tightly integrated DSP processor and a microcontroller architecture for a significant reduction in power for the intelligence of embedded machines.

At Arm Tech Con today, West Lake Village, Calif.-based startup Eta Compute showed off what it believes is the first commercial low-power AI chip capable of learning on its own using a type of machine learning called spiking neural networks. Most AI chips for use in low-power or battery-operated IoT devices have a neural network that has been trained by a more powerful computer to do a particular job. A neural network that can do what's called unsupervised learning can essentially train itself: Show it a pack of cards and it will figure out how to sort the threes from the fours from the fives. 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.