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 Blogs
- ChiPy®: Bridge Neural Networks and C++ on Silicon — Full Inference Pipelines with Zero CPU Round-Trips
- FPGAs take on convolutional neural networks
- Why the PowerVR 2NX NNA is the future of neural net acceleration
- Self-Compressing Neural Networks
Latest Blogs
- Ensuring reliability in Advanced IC design
- A Closer Look at proteanTecs Health and Performance Management Solutions Portfolio
- Enabling Memory Choice for Modern AI Systems: Tenstorrent and Rambus Deliver Flexible, Power-Efficient Solutions
- Verification Sanity in Chiplets & Edge AI: Avoid the “Second Design” Trap
- Embedded Security explained: Cryptographic Hash Functions