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
- Why the PowerVR 2NX NNA is the future of neural net acceleration
- Self-Compressing Neural Networks
- Efficient inference on IMG Series4 NNAs
- Embedded Vision: The Road Ahead for Neural Networks and Five Likely Surprises
Latest Blogs
- CEO Interview with Cyril Sagonero of Keysom
- Cycuity Partners with SiFive and BAE Systems to Strengthen Microelectronics Design Supply Chain Security
- Cadence Unveils the Industry’s First eUSB2V2 IP Solutions
- Half of the Compute Shipped to Top Hyperscalers in 2025 will be Arm-based
- Industry's First Verification IP for Display Port Automotive Extensions (DP AE)