In this installment we will investigate how a cloud processing system is structured to address and react to artificial intelligence technologies; how a GPU and CPU (central processing unit) compare ...

Eight years after the first mobile NPUs, fragmented tooling and vendor lock-in raise a bigger question: are dedicated AI ...

Hardware requirements vary for machine learning and other compute-intensive workloads. Get to know these GPU specs and Nvidia GPU models. Chip manufacturers are producing a steady stream of new GPUs.

A new survey paper describing Micron’s Automata Processor (AP) was recently published. AP has many potential applications in data mining, bioinformatics, natural language processing, etc. Micron has ...

Conrad Sanderson does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond ...

The difference between a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit) primarily lies in their design and functionality. CPUs are designed to handle a wide range of computing ...

What if the key to unlocking faster, more efficient machine learning workflows lies not in your algorithms but in the hardware powering them? In the world of GPUs, where raw computational power meets ...

In modern CPU device operation, 80% to 90% of energy consumption and timing delays are caused by the movement of data between the CPU and off-chip memory. To alleviate this performance concern, ...