The combination of edge-based ML running on the ultra-low power cores supports the increasing demand for pervasive intelligence in advanced audio, voice and sensing applications.
The HiFi DSPs are the first DSPs to support TensorFlow Lite for Microcontrollers. With the addition of optimised software support for TensorFlow Lite operators on the HiFi DSP cores, developers will now be able to take full advantage of the TensorFlow platform. This promotes rapid development of edge applications that use artificial intelligence (AI) and ML, removing the need for hand-coding the neural networks and resulting in faster time to market and higher performance.
Implementing AI at the edge, including devices that use voice and audio as a user interface, requires running the inference model on the device, the benefits of which include:
- Eliminates the latency associated with sending data to a cloud service and waiting for the response to be sent back to the device
- Reduces power consumption associated with sending/receiving large amounts of data across a network
- Maintains privacy and reduces security issues since the data never leaves the device
- Without cloud dependency, the device can be disconnected from the network and still operate
“Voice and audio AI applications are now mainstream, as voice-based user interfaces become more popular with consumers,” said Ian Nappier, product manager at Google. “TensorFlow Lite’s microcontroller software combined with optimised operators for the HiFi DSP makes developing and deploying innovative neural nets on low-power, memory-constrained audio DSPs easier than ever.”
“Enabling ML at the edge saves power, protects privacy and greatly reduces latency,” observed Yipeng Liu, director of audio/voice IP at Cadence. “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.”
