SANTA CLARA, Calif.--(BUSINESS WIRE)--Meta’s VP of Infrastructure Hardware, Alexis Black Bjorlin, will open the flagship AI Hardware Summit with a keynote, while her colleague Vikas Chandra, Meta’s Director of AI Research will open Edge AI Summit. Other notable keynotes include Microsoft Azure’s CTO, Mark Russinovich, plus Wells Fargo’s EVP of Model Risk, Agus Sudjianto; Synopsys’ President & COO, Sassine Ghazi; Cadence’s Executive Chairman, Lip-Bu Tan; and Siemens’ EVP, IC EDA, Joseph Sawicki, among many others…
Machine learning and deep learning are fast becoming major line items on agendas in board rooms in every organization across the globe. The technology stack needed to support these workloads, and to execute them quickly, efficiently, and affordably, is fast developing in both the datacenter and in client systems at the edge.
In 2018, a new Silicon Valley event called the AI Hardware Summit launched to provide a platform to discuss innovations in hardware necessary for supporting machine learning both at the very large scale, and in small resource-constrained environments. The event attracted enormous interest from the semiconductor and systems sectors, welcomed Habana Labs into the industry in its inaugural year, and subsequently hosted Alphabet Inc.’s Chairman and Turing Award Winner, John L. Hennessy, as a keynote speaker in 2019. Shortly after, the Edge AI Summit was launched to focus specifically on deploying machine learning in commercial use cases in client systems.
Hennessy said of the AI Hardware Summit: “It’s a great place where lots of people interested in AI Hardware are coming together and exchanging ideas, and together we make the technology better. There’s a synergistic effect at these summits which is really amazing and powers the entire industry.”
Fast forward a few years of virtual shows and the events are back in-person with a fresh angle. An all-star cast of tech visionary speakers will address optimizing and accelerating machine learning hardware and software, focusing on the intersection between systems design and ML development. Developer workshops with HuggingFace are a new feature this year focused on helping bring new hardware innovation into leading enterprises.
The co-location of the two industry-leading summits combines the proposition to focus on building, optimizing and unifying software-defined ML platforms across the cloud-edge continuum. Attendees of the AI Hardware Summit can expect content spanning from hardware and infrastructure up to models/applications, whereas the Edge AI Summit has a much tighter focus on case studies of ML in enterprise.
This year’s audience will consist of machine learning practitioners and technology builders from various engineering disciplines, discussing topics such as systems-first ML, AI acceleration as a full-stack endeavour, software defined systems co-design, boosting developer efficiency, optimizing applications across diverse ML platforms and bringing state of the art production performance into the enterprise.
While the AI Hardware Summit has broadened its scope beyond focusing purely on hardware, there will still be plenty for hardware-focused attendees to explore. The event website, www.aihardwaresummit.com, gives accessible information on why a software-focused or hardware-focused attendee should register.
The Edge AI Summit features more end user use cases than any other event of its kind, and is a must attend for anyone moving ML workloads to the edge. The event website, www.edgeaisummit.com, gives more information.