Eta Compute, a company that looks to deliver machine learning to low power IoT and edge devices using its TENSAI Platform, has announced its TENSAI Flow software suite. The suite complements Eta's existing development resources and enables seamless design from concept to firmware, speeding the creation of machine learning applications 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 of Eta Compute. Now, designers can optimise 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." Eta Compute's TENSAI Flow software has been designed to de-risk development by quickly confirming feasibility and proof of concept.

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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.