Loading...
You are here:  Home  >  Education Resources For Use & Management of Data  >  Data Daily | Data News  >  Current Article

AI Semiconductor Company Syntiant Demonstrates Analog Neural Network

By   /  May 11, 2018  /  No Comments

by Angela Guess

According to a recent press release, “Syntiant Corp., an artificial intelligence semiconductor company that is accelerating the transition of machine learning from the cloud to edge devices, today unveiled its ultra-low-power analog neural network technology at the Intel Capital Global Summit in Palm Springs, Calif… Ideally suited for voice, sensor and video applications from mobile phones and wearable devices to smart sensors and drones, Syntiant’s Neural Decision Processors (NDPs) utilize custom analog neural networks to optimize deep learning functions at the transistor level. Syntiant’s technology eliminates data movement penalties, enabling smart devices to perform neural computation locally, faster, and more efficiently than traditional CPU, GPU and DSP options. Syntiant NDPs are focused on always-on applications for battery-powered devices, such as keyword spotting, speaker identification, wake word, event detection, image recognition and sensor synthesis.”

Kurt Busch, chief executive officer of Syntiant, commented, “Analog neural networks and deep learning are a match made in heaven, delivering more than 50 times improvement in efficiency versus traditional digital stored-program architectures… Our technology combines the latest in deep learning research with Syntiant’s leadership team’s more than 10 decades of semiconductor experience to deliver transformative functionality for battery powered devices – from ear buds to cell phones – at microwatt power levels. Ultimately, this will enable OEMs to bring machine learning and artificial intelligence functionality to smart devices free from cloud connection, size, and power consumption constraints.”  

Read more at Globe Newswire.

Photo credit: Syntiant

You might also like...

Data Science in 90 Seconds: Support Vector Machines

Read More →