Efficient Machine Learning on HiFi DSPs Using TensorFlow
This presentation will walk through the details of building a keyword-detection model and deploying it on a HiFi DSP using the TensorFlow tool chain.
Looking for the best quality audio, voice, and/or speech processing core for your next design? With all the software already ported for immediate deployment? With the largest selection of audio DSPs, software packages, and ecosystem of partners, adding audio to your next SoC couldn’t be easier. Make the choice that more than 150 licensees have already made—select Cadence® Tensilica® HiFi DSPs for all your audio-, voice-, and speech-processing requirements.
In addition to traditional DSP workloads, the Tensilica HiFi DSP is increasingly being tasked with running machine learning applications. The machine learning functions in the world of audio include wake word detection, audio scene detection, speech recognition, and noise reduction. In order to reduce the time to market for these neural network (NN) tasks, the HiFi DSP software offering has been expanded to include new optimized libraries.
Machine learning applications must be energy and memory efficient to be deployed broadly in Tensilica HiFi DSP-based systems. The HiFi DSPs' support for TensorFlow Lite for Microcontrollers allows for timely development of these inference-at-the-edge solutions for resource-constrained embedded systems. The Tensilica HiFi DSPs' optimized libraries ensure optimal performance.
Cadence has your audio software needs covered with over 150 partners in its comprehensive audio ecosystem. These companies range from industry giants like Dolby and DTS to smaller companies providing innovative sensor fusion, always-on, sound-enhancement, and noise-reduction software.
We have more than 300 audio, voice, speech recognition, and voice enhancement software packages already ported to the HiFi DSP architecture. This means you can get up and running very quickly, and can easily port your own proprietary software, completely in C, while also maintaining or surpassing the performance of assembly on other DSPs.
Optimized math libraries to simplify programming/porting DSP algorithms
Highly optimized functions commonly used in speech NN processing
Most comprehensive set of audio/speech codecs in the industry
Software framework to offload and manage audio/voice/NN processing to DSP
ML Framework
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