AI inference hardware startup, Untether AI, has secured a fresh $125 million in funding to push its novel architecture into its first commercial customers in edge and datacenter environments. …
The mighty SoC is coming for the datacenter with inference as a prime target, especially given cost and power limitations. …
When it comes to neural network training, Python is the language of choice. …
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. …
The hyperscalers and cloud builders have been setting the pace for innovation in the server arena for the past decade or so, particularly and publicly since Facebook set up the Open Compute Project in April 2011 and ramping up as Microsoft joined up in early 2014 and basically created a whole new server innovation stream that was unique from – and largely incompatible with – the designs put out by Facebook. …
There is much at stake in the world of datacenter inference and while the market has not yet decided its winners, there are finally some new metrics in the bucket to aid decision-making. …
It would be convenient for everyone – chip makers and those who are running machine learning workloads – if training and inference could be done on the same device. …
Processor hardware for machine learning is in their early stages but it already taking different paths. …
There are two trends converging in AI inference and so far, only a small number of companies are enmeshed. …
In the deep learning inferencing game, there are plenty of chipmakers, large and small, developing custom-built ASICs aimed at this application set. …
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