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CEVA's DSP and Voice Neural Networks integrated with TensorFlow Lite

CEVA-BX DSP cores and WhisPro speech recognition software targeting conversational AI and contextual awareness applications now support TensorFlow Lite for Microcontrollers.

TensorFlow Lite is a production ready, cross-platform framework for deploying tiny machine learning on power-efficient processors in edge devices.

Tiny machine learning brings 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 approach to AI at the edge ensures that customers using TensorFlow Lite for Microcontrollers can use 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, helping to accelerate the development of small footprint voice assistants and other voice controlled IoT devices.

Erez Bar-Niv, Chief Technology Officer at CEVA, said, “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 for Microcontrollers dramatically simplifies the development of these devices, by providing a lean framework to deploy machine learning models on resource-constrained processors.

"With full optimisation of this framework for our CEVA-BX DSPs and our WhisPro speech recognition models, we are lowering the entry barrier for SoC companies and OEMs to add intelligent sensing to their devices.”

The CEVA-BX DSP family is a high-level programmable hybrid DSP/controller offering high efficiency for a broad range of signal processing and control workloads of real-time applications.

Using an 11-stage pipeline and 5-way VLIW micro-architecture, it offers parallel processing with dual scalar compute engines, load/store and program control that reaches a CoreMark per MHz score of 5.5, making is suitable for real time signal control. Its support for SIMD instructions makes it suitable for a wide variety of signal processing applications and the double precision floating point units efficiently handle contextual awareness and sensor fusion algorithms with a wide dynamic range.

It also facilitates simultaneous processing of front-end voice, sensor fusion, audio processing, and general DSP workloads in addition to AI runtime inferencing, allowing customers and algorithm developers to take advantage of CEVA’s extensive audio, voice and speech machine learning software and libraries to accelerate their product designs.

Author
Neil Tyler

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