Cadence Tensilica HiFi IP Now Supports TensorFlow Lite for Microcontrollers
Cadence Design Systems has optimized the software for Cadence Tensilica HiFi digital signal processors (DSPs) to execute TensorFlow Lite for Microcontrollers, part of the TensorFlow end-to-end open-source platform for machine learning (ML) from Google.
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.
Implementing AI at the edge, including devices that use voice and audio as a user interface, requires running the inference model on the device. This has the following benefits:
- 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 minimizes security issues since the data never leaves the device; and
- 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, in a statement. "TensorFlow Lite's microcontroller software combined with optimized 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," said Yipeng Liu, director of audio/voice IP at Cadence, in a statement. "Tensilica HiFi DSPs are the most widely licensed DSPs for audio, voice, and AI speech. 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."
CEVA's WhisPro speech recognition is now available with open-source TensorFlow Lite for Microcontrollers implementing machine learning at the edge.