“The current ML problems using 32-bit dense matrix multiplication is where GPUs excel. We encourage other developers and researchers to join forces with us to reformulate machine learning problems to ...

“At the end of the day, everyone needs to be a bit realistic here,” says Cantle. “In terms of price/performance, FPGA cards do not sell in the volumes of GPU cards, so we hit a price/performance limit ...

GPUs and FPGAs are current technologies that are helping to solve challenges in how to expand the impact of machine learning on many markets.

An FPGA can do anything… But slower, more expensive, and with more power. I do love FPGAs and enjoy working with them. The flexibility is fantastic, but it takes an order of magnitude more ...

Field-programmable gate arrays (FPGAs) are versatile silicon chips that are proving to be extremely fast at certain operations. Even though GPUs run at 1GHz+ and have thousands of stream ...

FPGA vs. GPU: Advantages and disadvantages To summarize these, I have provided four main categories: Raw compute power, Efficiency and power, Flexibility and ease of use, and Functional Safety. The ...

One salient feature of SC15, the US supercomputing conference in Austin, Texas, at the end of November was the way in which FPGAs are now coming into the limelight, in a manner markedly similar to the ...

Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) are the two main hardware solutions for most AI operations. According to the precedence research group, the global AI in ...

Ubitium announces development of 'universal' processor that combines CPU, GPU, DSP, and FPGA functionalities – RISC-V powered chip slated to arrive in two years ...

FPGAs, notable because they can be reprogrammed for new processing tasks, seem to have lost their luster in the mania around generative AI. GPUs are all the rage, or in some cases, custom silicon ...