AI acceleration is a full stack effort and involves a multidisciplinary and holistic approach to design and optimization.
The field of deep learning has gained substantially from co-design concepts across the AI technology stack. The simultaneous design and optimization of hardware and software has led to new algorithms, numerical optimizations, and AI hardware.
Looking at the AI stack for workloads like computer vision, NLP and Ads, in both a vertical and horizontal sense, there are significant opportunities and challenges for optimization through co-design. This panel will focus on software-defined chips and systems for AI (specs & evaluation, datacenter & edge) and look at the systems-level approach to co-design, including compilers and runtime etc.
Nick Ni
Nick Ni is Senior Director, Data Center AI and Compute Markets at Adaptive Embedded Computing Group (AECG) at AMD, responsible for the P&L of the fast-growing Data Center AI and compute segment. His team is responsible for product marketing and product management including AI product planning, go-to-market, business development and solution architecture.
Xiaoyong Liu
Shubho Sengupta
Shubho Sengupta is a Software Engineer at Meta, where he designs Meta’s Research Infra for AI training. He started working on AI in 2014, on speech related AI models like DeepSpeech and DeepVoice. Before that he pioneered many of the foundational algorithms in general purpose programming in GPUs, which has won Test of Time award. These days, he also works at the intersection of cryptography and computation, specifically in bi-partite and multi-partite matching algorithms.
Dr. Charles Fan
Charles Fan is CEO and co-founder of MemVerge. Prior to MemVerge, Charles was the CTO of Cheetah Mobile leading its global technology teams, and an SVP/GM at VMware, founding the storage business unit that developed the Virtual SAN product. Charles also worked at EMC and was the founder of the EMC China R&D Center. Charles joined EMC via the acquisition of Rainfinity, where he was a co-founder and CTO. Charles received his Ph.D. and M.S. in Electrical Engineering from the California Institute of Technology, and his B.E. in Electrical Engineering from the Cooper Union.
Zaid Kahn
Zaid is currently a VP in Microsoft’s Silicon, Cloud Hardware, and Infrastructure Engineering organization where he leads systems engineering and hardware development for Azure including AI systems and infrastructure. Zaid is part of the technical leadership team across Microsoft that sets AI hardware strategy for training and inference. Zaid's teams are also responsible for software and hardware engineering efforts developing specialized compute systems, FPGA network products and ASIC hardware accelerators.
Prior to Microsoft Zaid was head of infrastructure at LinkedIn where he was responsible for all aspects of architecture and engineering for Datacenters, Networking, Compute, Storage and Hardware. Zaid also led several software development teams focusing on building and managing infrastructure as code. This included zero touch provisioning, software-defined networking, network operating systems (SONiC, OpenSwitch), self-healing networks, backbone controller, software defined storage and distributed host-based firewalls. The network teams Zaid led built the global network for LinkedIn, including POP's, peering for edge services, IPv6 implementation, DWDM infrastructure and datacenter network fabric. The hardware and datacenter engineering teams Zaid led were responsible for water cooling to the racks, optical fiber infrastructure and open hardware development which was contributed to the Open Compute Project Foundation (OCP).
Zaid holds several patents in networking and is a sought-after keynote speaker at top tier conferences and events. Zaid is currently the chairperson for the OCP Foundation Board. He is also currently on the EECS External Advisory Board (EAB) at UC Berkeley and a board member of Internet Ecosystem Innovation Committee (IEIC), a global internet think tank promoting internet diversity. Zaid has a Bachelor of Science in Computer Science and Physics from the University of the South Pacific.