Press release
CEVA Announces DSP and Voice Neural Networks Integration with TensorFlow Lite for Microcontrollers
-- WhisPro™ speech recognition software for voice wake words and custom command models now available with open source TensorFlow Lite for Microcontrollers
About this update from Ceva, Inc.
[{"type":"text","content":"-- WhisPro™ speech recognition software for voice wake words and custom command models now available with open source TensorFlow Lite for Microcontrollers implementing machine learning at the edge\n -- TensorFlow Lite for Microcontrollers from Google is already optimized and available for CEVA-BX DSP cores, accelerating the use of low power AI in conversational and contextual awareness applications\n\n\nMOUNTAIN VIEW, Calif., March 24, 2020 /PRNewswire/ -- CEVA, Inc. (NASDAQ: CEVA), the leading licensor of wireless connectivity and smart sensing technologies, today announced that its CEVA-BX DSP cores and WhisPro™ speech recognition software targeting conversational AI and contextual awareness applications now also support TensorFlow Lite for Microcontrollers, a production ready, cross-platform framework for deploying tiny machine learning on power-efficient processors in edge devices. \nTiny machine learning brings the power of AI to extremely low power, always-on, battery operated IoT devices for on-device sensor data analytics in areas such as audio, voice, image and motion. CEVA's holistic approach to AI at the edge ensures that customers using TensorFlow Lite for Microcontrollers can utilize a unified processor architecture to run both the framework and the associated neural network workloads required to build these intelligent connected products. CEVA's WhisPro speech recognition software and custom command models are integrated with the TensorFlow Lite framework, further accelerating the development of small footprint voice assistants and other voice controlled IoT devices.\nPete Warden, Technical Lead of TensorFlow at Google commented: \"CEVA has been at the forefront of machine learning and neural networks inferencing for embedded systems and understands that the future of ML is Tiny going into extremely power and cost constrained devices. Their continued investment into powerful architectures, tools and software which support TensorFlow models provide a compelling offering for a new generation of intelligent embedded devices to harness the power of AI.\"\nErez Bar-Niv, Chief Technology Officer at CEVA, stated: \"The increasing demand for on-device AI to augment contextual awareness and conversational AI workloads poses new challenges to the cost, performance and power efficiency of intelligent devices. TensorFlow Lite ...