Collaborate Disseminate
Have you wanted to get into GPU programming with CUDA but found the usual textbooks and guides a bit too intense? Well, help is at hand in the form of …read more Continue reading Learn GPU Programming With Simple Puzzles
Compute Unified Device Architecture, or CUDA, is a software platform for doing big parallel calculation tasks on NVIDIA GPUs. It’s been a big part of the push to use GPUs …read more Continue reading CUDA, But Make It AMD
Readers are likely familiar with photogrammetry, a method of creating 3D geometry from a series of 2D photos taken of an object or scene. To pull it off you need …read more Continue reading NeRF: Shoot Photos, Not Foam Darts, to See Around Corners
In the old days, big computers often had some form of external array processor. The idea is you could load a bunch of numbers into the processor and then do …read more Continue reading What Kind of GPU Are You?
Prosthetic limbs have been slow to evolve from simple motionless replicas of human body parts to moving, active devices. A major part of this is that controlling the many joints …read more Continue reading Deep Learning Enables Intuitive Prosthetic Control
NVIDIA kicked off their line of GPU-accelerated single board computers back in 2014 with the Jetson TK1, a $200 USD development system for those looking to get involved with the burgeoning world of so-called “edge computing”. It was designed to put high performance computing in a small and energy efficient …read more
Continue reading NVIDIA Announces $59 Jetson Nano 2GB, a Single Board Computer with Makers in Mind
The word supercomputer gets thrown around quite a bit. The original Cray-1, for example, operated at about 150 MIPS and had about eight megabytes of memory. A modern Intel i7 CPU can hit almost 250,000 MIPS and is unlikely to have less than eight gigabytes of memory, and probably has quite a bit more. Sure, MIPS isn’t a great performance number, but clearly, a top-end PC is way more powerful than the old Cray. The problem is, it’s never enough.
Today’s computers have to processes huge numbers of pixels, video data, audio data, neural networks, and long key encryption. Because …read more
Identifying ham radio signals used to be easy. Beeps were Morse code, voice was AM unless it sounded like Donald Duck in which case it was sideband. But there are dozens of modes in common use now including TV, digital data, digital voice, FM, and more coming on line every day. [Randaller] used CUDA to build a neural network that could interface with an RTL-SDR dongle and can classify the signals it hears. Since it is a neural network, it isn’t so much programmed to do it as it is trained. The proof of concept has training to distinguish FM, …read more
