Leo Lathi
Associate Professor
University of Turku
Lawrence Spracklen
Director of Machine Learning Architecture
Numenta
Prasad Akella
Chairman and Founder
Drishti
Dr. Prasad Akella, founder and chairman of the Drishti board, is creating his third massive market category that uses technology to extend human capabilities. In the 1990s, Prasad led the General Motors team that built the world’s first collaborative robots (“cobots,” projected to be a $12B market by 2025). In the early 2000s, as cofounder of the social networking pioneer Spoke, he envisioned and helped build the first massive social graph — a category now worth trillions. Today, at Drishti, he is working to combine the cognition of AI with the flexibility of humans in factories in the form of AI-powered production. Prasad is based in Mountain View, California.
Alessya Visnjic
CEO and Co-Founder
WhyLabs.ai
Alessya Visnjic is the CEO of WhyLabs, the AI Observability company building the interface between AI & human operators. Prior to WhyLabs, Alessya was a CTO-in-residence at the Allen Institute for AI, where she evaluated commercial potential for the latest AI research. Earlier, Alessya spent 9 years at Amazon leading ML adoption & tooling efforts. Alessya is also the founder of Rsqrd AI, a global community of 1,000+ AI practitioners who are making AI technology Robust & Responsible.
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.
Chip Design
Novel AI Hardware
Systems Design
Hardware Engineering
Software Engineering
Systems Engineering
Nick Ni
Senior Director, Datacenter AI & Compute Markets
AMD
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
Director, AI Platform
Alibaba
Shubho Sengupta
Software Engineer
Meta
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
CEO and Co-Founder
MemVerge
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
VP, Cloud AI & Advanced Systems Engineering
Microsoft
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.
As scientific and machine learning workloads converge in the world of HPC, and supercomputing centers gear up for the era of exascale computing, discussions on heterogeneous systems design abound. HPC leaders increasingly need to support converged application workloads that extend beyond AI/HPC to include other computational kernels/patterns like data analytics, graph algorithms, and uncertainty quantification. In this sector, the value of heterogeneity in systems design is clear and promising, even if the method for executing these concepts is still to be determined.
However, in many industrial sectors, enterprise end customers simply use the 'threat' of heterogeneity as a tool to extract some discount from their main/incumbent vendor. The job of IT is hard enough, planning for compute, storage and networking needs, that adding a lot of compute specialization is often not high on a CIO’s priority list.
So, who cares about heterogeneity? Where will heterogeneity in systems design change the game, and what will be its level and quality?
Chip Design
ML at Scale
Novel AI Hardware
Systems Design
Hardware Engineering
Strategy
Systems Engineering
Wahid Bhimji
Acting Group Lead, Data & Analytics
NERSC
Wahid Bhimji is acting Group Lead and a Big Data Architect in the Data and Analytics Services Group at NERSC. His interests include machine learning and data management. Recently he led several projects applying AI for science including deep learning at scale, generative models and probabilistic programming. He coordinates aspects of machine learning deployment for the Lab's CS-Area and NERSC: including the upcoming Perlmutter HPC system and plans for future NERSC machines. Previously he was user lead for the commissioning of Cori Phase 1, particularly data services, and for the Burst Buffer. Wahid has worked for many years in Scientific Computing and Data Analysis in Academia and the U.K. Government and has a Ph.D. in High-Energy Particle Physics.
Weifeng Zhang
Chief Scientist, Heterogeneous Computing
Alibaba
Weifeng Zhang is the Chief Scientist of Heterogeneous Computing at Alibaba Cloud Infrastructure, responsible for performance optimization of large scale distributed applications at the data centers. Weifeng also leads the effort to build the acceleration platform for various ML workloads via heterogeneous resource pooling based on the compiler technology. Prior to joining Alibaba, Weifeng was a Director of Engineering at Qualcomm Inc, focusing on GPU compiler and performance optimizations. Weifeng received his B.Sc. from Wuhan University, China and PhD in Computer Science from University of California, San Diego.
Cedric Bourrasset
Head, High Performance AI Business Unit
Atos
Dr. Cedric Bourrasset is AI Business Leader for High Performance Computing Business Unit at Atos. He is also AI product manager for the Atos Codex AI suite, software enabling AI workloads into HPC environments as well as integrating a computer vision solution. He joined Atos in 2016 as an expert in the HPC/AI domain.
Previously, Cedric received his Ph.D. in Electronics and computer vision from the Blaise Pascal University of Clermont-Ferrand defending the dataflow model of computation for FPGA High Level Synthesis problematic in embedded machine learning applications.
Bhupender Thakur
Product Manager, Scientific Computing
Roche
Bhupender Thakur is Product portfolio owner for several High Performance and Big Data platforms for research and early development at Roche. He is the Agile product portfolio owner of on-premise HPC services delivering compute and storage clusters in several locations across the USA, Germany and Switzerland, and product owner for workflow applications for NGS and Oncology research supporting Roche Avenio product offerings.
Bhupender leads a cross functional squad of developers, product owners, architects and subject matter experts, working on roadmaps for existing and new research offerings and leading discussions on planning, lifecycle, operations and business continuity.
He holds a PhD in Theoretical and Computational Nuclear Physics from the University of Delaware.
In this keynote, Dr. Cédric Bourrasset, AI Distinguished Expert at Atos, will reveal how Atos pioneered the successful architecture, build, and delivery of large-scale AI infrastructures. He will present a live demonstration of Atos-driven technology to illustrate new AI-driven endpoints featuring GPU and IPU workflow capabilities, featuring a global customer case study to elaborate on the current complex challenges faced by designing and manufacturing large-scale AI computing platforms. He will also leverage over 15 years of personal experience in designing and manufacturing supercomputing systems.
Developer Efficiency
Edge AI
Enterprise AI
ML at Scale
Novel AI Hardware
Systems Design
Data Science
Hardware Engineering
Software Engineering
Strategy
Systems Engineering
Cedric Bourrasset
Head, High Performance AI Business Unit
Atos
Dr. Cedric Bourrasset is AI Business Leader for High Performance Computing Business Unit at Atos. He is also AI product manager for the Atos Codex AI suite, software enabling AI workloads into HPC environments as well as integrating a computer vision solution. He joined Atos in 2016 as an expert in the HPC/AI domain.
Previously, Cedric received his Ph.D. in Electronics and computer vision from the Blaise Pascal University of Clermont-Ferrand defending the dataflow model of computation for FPGA High Level Synthesis problematic in embedded machine learning applications.
Atos
Website: https://atos.net/thinkai
Atos is a global leader in digital transformation with 109,000 employees and annual revenue of c. € 11 billion. European number one in cybersecurity, cloud and high performance computing, the Group provides tailored end-to-end solutions for all industries in 71 countries. A pioneer in decarbonization services and products, Atos is committed to a secure and decarbonized digital for its clients. Atos is a SE (Societas Europaea), listed on Euronext Paris and included in the CAC 40 ESG and Next 20 Paris Stock indexes.
The purpose of Atos is to help design the future of the information space. Its expertise and services support the development of knowledge, education and research in a multicultural approach and contribute to the development of scientific and technological excellence. Across the world, the Group enables its customers and employees, and members of societies at large to live, work and develop sustainably, in a safe and secure information space.