i'm guessing they will be available in a month or so - they are supposed to "Q2" but seem to be a little bit late (as is typical).
https://www.en.alinx.com/Product/SoC-Development-Boards/Vers...
GPUs tend to perform worse when you have small batches and frequent kernel launches. This is especially annoying in cases where a simple kernel wide synchronization barrier could solve your problems, but CUDA expects you to not synchronize like that within the kernel, you're supposed to launch a sequence of kernels one after the other. That's not a good solution if a for loop over n iterations turns into n kernel calls.
> Thanks to the extensive work of the MiSoC and LiteX crowd, there’s already IP cores for DRAM, PCI express, ethernet, video, a softcore CPU (your choice of or1k or lm32) and more.. LiteX produces a design that uses about 20% of an XC7A50 FPGA with a runtime of about 10 minutes, whereas Vivado produces a design that consumes 85% of the same FPGA with a runtime of about 30-45 minutes.. LiteX, in its current state, is probably best suited for people trained to write software who want to design hardware, rather than for people classically trained in circuit design who want a tool upgrade.
> the smallest, lowest power, and most cost-optimized member of the Zynq UltraScale+ family.. jump-start.. MPSoC-based end systems like miniaturized, compute-intensive edge applications in industrial and healthcare IoT systems, embedded vision cameras, AV-over-IP 4K and 8K-ready streaming, hand-held test equipment, consumer, medical applications and more.. board is ideal for design engineers, software engineers, system architects, hobbyists, makers and even students
You aren't "programming", you're "wiring gates together". In other words, you can build custom hardware to solve a problem without using a generic CPU (or GPU) to do it. FPGAs are implemented as a fabric of LUTs (Look-up Tables) which take 4- or 6- (or more) inputs and produce an output. That allows Boolean algebra functions to be processed. The tools you use (Vivado / ISE / YoSys / etc.) take a your intended design, written in a HDL (Hardware Design Language) such as Verilog or VHDL, and turn it into a configuration file which is injected into the FPGA, causing it to be configured to into the hardware you want (if you've done it right). FPGAs are a stepping stone between generic hardware such as a CPU or GPU and a custom ASIC. They win when you can express the problem in specialized hardware much better than writing code to do something on a CPU/GPU. Parallelization is the key to many FPGA designs. Also, you don't have to spend >$1MM on a mask set to go have an ASIC fabricated by TSMC, etc.
Given the density of the PDF, I saw AMD and AI in the title and assumed the scientific community was trying to get AMD GPUs to work. This makes more sense.
Via Xilinx $49B acquisition, https://www.crn.com/news/components-peripherals/amd-complete...
I’m waiting for the similar cost reduction that happened to Ultrascale+ devices and we finally got something like the ZuBoard