Latest Articles (10+)
Neural Network Architectures at the Edge: Modelling for Energy Efficiency and Machine Learning Performance
A short guide to understanding the edge neural network design.
Semantic-based neural network ontology for TinyML that faces challenges discovering appropriate combinations and initiating deployment.
Over the last decade, deep neural networks have emerged as the solution to several AI complex applications from speech recognition and object detection to autonomous vehicular systems.
A system with an opto-chemical sensor combined with a battery-free NFC tag of resolution 103 ppm for measuring the CO2 gas concentration in the dead space volume of the facemask
FaceBit aims to be low-cost, energy-efficient, and easy-to-integrate hardware with rich sensing functionalities– monitors physiological signals
Heterogenous analog in-memory compute architecture integrating 8 RISC-V cores along with an IMC accelerator and depth-wise digital accelerator.
A rich line-up of speakers discuss the trends and challenges of the industry.
Utilizing audio edge processors for efficient execution of Machine Learning Tasks.
A closer look at the recent market trends and audio technologies that enhance the functionalities of TWS devices.
Making the leap from “Smart” to “Useful”. Smart home devices will continue to increase their utility as the challenges of audio source classification are solved by audio edge processors specifically designed for advanced audio and machine learning applications.
#4 of our 'Voice of Innovation' fireside chat series, in which Syntiant CEO Kurt Busch talks with Andreas Urschitz Division President Power & Sensor Systems at Infineon.
Dedicated audio edge processors with machine learning will deliver increasingly natural communication experiences.