After its acquisitions of ATI in 2006 and the maturation of its discrete GPUs with the Instinct line from the past few years and the acquisitions of Xilinx and Pensando here in 2022, AMD is not just a second source of X86 processors. …
Nvidia is not the only company that has created specialized compute units that are good at the matrix math and tensor processing that underpins AI training and that can be repurposed to run AI inference. …
Industry benchmarks are important because, no matter that comparisons are odious, IT organizations nonetheless have to make them to plot out the architectures of their future systems. …
As Google’s batch sizes for AI training continue to skyrocket, with some batch sizes ranging from over 100k to one million, the company’s research arm is looking at ways to improve everything from efficiency, scalability, and even privacy for those whose data is used in large-scale training runs. …
Any workload that has a complex dataflow with intricate data needs and a requirement for low latency should probably at least consider an FPGA for the job. …
Processor hardware for machine learning is in their early stages but it already taking different paths. …
When it comes to machine learning training, people tend to focus on the compute. …
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