Recently, we wrote about one of the most exciting emerging technology areas, machine learning, and the question of what role the XPRTs could play in the field.
Experts expect machine learning to be the analytics backbone of the IoT data explosion. It is a disruptive technology with potential to influence a broad range of industries. Consumer and industrial applications that take advantage of machine-learning advancements in computer vision, natural language processing, and data analytics are already available and many more are on the way.
Currently, there is no comprehensive machine-learning or deep-learning benchmark that includes home, automotive, industrial, and retail use cases. The challenge with developing a benchmark for machine learning is that these are still the early days of the technology. A fragmented software and hardware landscape and lack of standardized implementations makes benchmarking machine learning complex and challenging.
Based on the conversations we’ve had over the last few weeks, we’ve decided to take on that challenge. With the community’s help, of course!
As we outlined in a blog entry last month, we will work with interested folks in the community, key vendors, and academia to pull together what we are internally calling MLXPRT.
While the result may differ substantially from the existing XPRTs, we think the need for something is great. Whether that will turn out to be a packaged tool or just sample code and workloads remains to be seen.
What we need most your help. We need both general input about what you would like to see as well as any expertise you may have. Let us know any questions you may have or ways you can help.
On a related note, I’ll be at CES 2017 in Las Vegas during the first week of January. I’d love to meet and talk more about machine learning, benchmarking, or the XPRTs. If you’re planning to be there and would like to connect, let us know.
We will not have a blog entry next week over the holidays, so we wish all of you a wonderful time with your families and a great start to the new year.
Bill
