Propping up a successful silicon startup is no simple feat, but venture-backed Wave Computing has managed to hold its own in the small but critical AI training chip market–so far. …
In the U.S. it is easy to focus on our native hyperscale companies (Google, Amazon, Facebook, etc.) …
In this fast-paced global economy, enhanced speed, productivity, and intelligence are more important than ever to success. …
In a properly working capitalist economy, innovative companies make big bets, help create new markets, vanquish competition or at least hold it at bay, and profit from all of the hard work, cleverness, luck, and deal making that comes with supplying a good or service to demanding customers. …
One of the luckiest coincidences in the past decade has been that the hybrid machines designed for traditional HPC simulation and modeling workloads. …
The golden grail of deep learning has two handles. On the one hand, developing and scaling systems that can train ever-growing model sizes is one concern. …
In the IT business, just like any other business, you have to try to sell what is on the truck, not what is planned to be coming out of the factories in the coming months and years. …
Google has been at the bleeding edge of AI hardware development with the arrival of its TPU and other system-scale modifications to make large-scale neural network processing efficient and fast. …
Custom accelerators for neural network training have garnered plenty of attention in the last couple of years, but without significant software footwork, many are still difficult to program and could leave efficiencies on the table. …
For developers, deep learning systems are becoming more interactive and complex. …
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