Software accelerates AI deployment in audio, voice and sensing devices
Software for Cadence Tensilica HiFi digital signal processors (DSPs) has been optimised to execute TensorFlow Lite for Microcontrollers, part of the TensorFlow open-source platform for machine learning (ML) from Google. The edge-based ML running on the low power cores supports intelligence in audio, voice and sensing applications.
The HiFi DSPs are the first DSPs to support TensorFlow Lite for Microcontrollers, says Cadence. The software support for TensorFlow Lite on the HiFi DSP cores, promotes development of edge applications that use artificial intelligence (AI) and ML on TensorFlow and removes the need for hand-coding neural networks. This accelerates time to market, Cadence notes.
Implementing AI at the edge on devices that use voice and audio as a user interface, requires the inference model to be run on the device. This eliminates the latency associated with sending data to a cloud service and waiting for the response to be sent back to the device and also reduces power consumption associated with sending/receiving large amounts of data across a network.
It also serves to maintain privacy and minimise security issues since the data does not leave the device. As the device is not dependent on the cloud, it can be disconnected from the network and still operate.
A 600MHz Tensilica HiFi 4 DSP is included in NXP Semiconductor’s i.MX RT600 and delivers 4.8 Giga multiply-accumulates per second (GMACS). It has the compute power required for deploying voice, audio and other neural network-based applications at the edge. Joe Yu, vice president of microcontrollers at NXP Semiconductors, said: “Supporting the popular, end-to-end toolchain, TensorFlow, as well as other inferencing technologies, on the HiFi DSP will enable ML developers to take advantage of the compelling combination of compute and memory on this chip”.
Yipeng Liu, director of audio/voice IP at Cadence, added: “Support for TensorFlow Lite for Microcontrollers enables our licensees to innovate with ML applications like keyword detection, audio scene detection, noise reduction and voice recognition, with the assurance that they can run in an extremely low-power footprint”.
