Community

In this guest blog post, from Kentaro Niikura, read about how Renesas is implementing an advancement in AI.

Most conventional FPGA-based accelerators use off-chip memory for data transformation, then perform computation for a single-layer in a time-multiplexed manner. Throughput is often limited by the memory bandwidth and the amount of available resources

How to convert models to run Ethos-U55 and Ethos-U65 for models trained in PyTorch.

This blog discusses the growth of smartwatches and how Arm is delivering processor power and efficiency capabilities for these devices.

Demand for compute is increasing, yet carbon emissions must be reduced. Machine learning can make workflows more efficient, reducing their carbon footprint.

TinyML seeks to deploy ML algorithms on ultra low power systems, to enable us to intelligently select which data to transmit, improving energy efficiency.