What Happens When Mass Multiply No Longer Defines AI Accelerators?
Current custom AI hardware devices are built around super-efficient, high performance matrix multiplication. …
Current custom AI hardware devices are built around super-efficient, high performance matrix multiplication. …
Everybody wants to build a platform, hence the name of this publication. …
The Edinburgh Parallel Computing Center (EPPC) is up and running with its Cerebras CS-1 waferscale system and is already working with European companies in biomedical and cybersecurity arenas in addition to its own research into different programming and AI models and projects in natural language processing and genome-wide association studies. …
There’s an AI unicorn startup you might not have even heard of. …
For a decade and a half, Nvidia has been pushed its way into the datacenter, making its presence felt with its GPU accelerators that are designed to improve the performance and power efficiency of servers in HPC and enterprise compute environments and also expanding the opportunities for running highly parallel workloads. …
For the past several years, tech giants have been trying to make artificial intelligence in its many guises HPC, data analytics, and other advanced workloads more available and easier to use for enterprises. …
Covering developments in neuromorphic computing has been something of a piecemeal experience, as happens with all novel architectures. …
The mighty SoC is coming for the datacenter with inference as a prime target, especially given cost and power limitations. …
If the world was not a complex place, and if all machine learning training looked more or less the same, then there would only be one accelerator to goose training workloads. …
With the AI chip startup hype cycle spinning down from its feverish pace in 2018, giving way to 2021 expectations for real-world deployments, it still difficult to see which company will steal what little share is left in the Nvidia/Intel/AMD dominated datacenter. …
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