Running LSTM neural networks on an Imagination NNA
Speech recognition has become more relevant in recent years: it enables computers to translate spoken language into text. It can be found in different types of applications, such as translators or closed captioning. An example of this technology is Mozilla’s DeepSpeech, an open-source speech-to-text engine, which uses a model trained by machine learning techniques based on Baidu’s Deep Speech research paper. We are going to provide an overview of how we are running version 0.5.1 of this model, by accelerating a static LSTM network on the Imagination neural network accelerator (NNA), with the goal of creating a prototype of a voice assistant for an automotive use case.
To read the full article, click here
Related Semiconductor IP
- Advanced graphics and compute acceleration on power constrained devices
- E-Series GPU IP
- High performance GPU for cloud gaming with DirectX support
- Ray tracing GPU
- PowerVR Series9XMP Graphic Processor
Related Blogs
- Why the PowerVR 2NX NNA is the future of neural net acceleration
- Self-Compressing Neural Networks
- ChiPy®: Bridge Neural Networks and C++ on Silicon — Full Inference Pipelines with Zero CPU Round-Trips
- Efficient inference on IMG Series4 NNAs
Latest Blogs
- Automated, Faster Specification to Sign-Off with IDS-AI
- NovaTech Automation Crius PIU: Bringing Conventional Instrument Transformers onto the IEC 61850 Process Bus
- Single Pair Ethernet and TSN: The In-Robot Network Behind the Next Humanoid Robots
- A design path to success exists for ultra-low-voltage SoCs
- Where Routine Flow Ends Veriest Formal Expertise Begins