Join this moderated roundtable discussion group of 10-20 attendees focusing on novel training and learning paradigms for ML.
There will be several moderators per topic area to allow for multiple tables and questions will be prepared in advance. Each group will be multidisciplinary with representation from across the tech stack. Attendees who have registered for the event will be able to sign up for the roundtable discussion groups closer to the event, or via an AI Hardware & Edge AI Summit sales representative.
The subtopics to be discussed will include:
- Limitations of current training architectures and paradigms
- Use cases and challenges for distributed learning (i.e. federated learning)
- Data centric AI + few & low shot learning methods for efficient training
- Datasets for ML training - Open Source options
Rochan Sankar
Rochan is Founder, President and CEO of Enfabrica. Prior to founding Enfabrica, he was Senior Director and leader of the Data Center Ethernet switch silicon business at Broadcom, where he defined and brought to market multiple generations of Tomahawk/Trident chips and helped build industry-wide ecosystems including 25G Ethernet and disaggregated whitebox networking.
Prior, he held roles in product management, chip architecture, and applications engineering across startup and public semiconductor companies. Rochan holds a B.A.Sc. in Electrical Engineering from the University of Toronto and an MBA from the Wharton School, and has 6 issued patents.
Enfabrica
Website: https://enfabrica.net/
Enfabrica is a cutting-edge silicon and software company building disruptive networking solutions for parallel, heterogeneous and accelerated computing infrastructure. Inventors of the Accelerated Compute Fabric SuperNIC (ACF-S), Enfabrica's groundbreaking chips, software stack design and partner-enabled systems are designed to scale GPU clusters faster and more efficiently than any other solution, while returning control of the AI network infrastructure stack to the customer. Enfabrica is elevating networking for the age of GenAI with the world's most advanced, performant and efficient solutions interconnecting compute, memory and network.
Mike Demler
Mike Demler is a longtime semiconductor industry veteran, technology analyst and strategic consultant. Over the last 10 years, Mike has authored numerous in-depth analyses of the innovative technologies driving advances in AI, ADAS, and autonomous vehicles. He co-authored five editions of the Linley Group Guide to Processors for Deep Learning, along with the Guide to Processors for Advanced Automotive. He now offers his insights as an advisor to clients across a broad spectrum of the technology industry.
Gayathri Radhakrishnan
Gayathri is currently Partner at Hitachi Ventures. Prior to that, she was with Micron Ventures, actively investing in startups that apply AI to solve critical problems in the areas of Manufacturing, Healthcare and Automotive. She brings over 20 years of multi-disciplinary experience across product management, product marketing, corporate strategy, M&A and venture investments in large Fortune 500 companies such as Dell and Corning and in startups. She has also worked as an early stage investor at Earlybird Venture Capital, a premier European venture capital fund based in Germany. She has a Masters in EE from The Ohio State University and MBA from INSEAD in France. She is also a Kauffman Fellow - Class 16.
Sakyasingha Dasgupta
Sakya is the founder and Chief Executive officer of EdgeCortix. He is an artificial intelligence (AI) and machine learning technologist, entrepreneur, and engineer with over a decade of experience in taking cutting edge AI research from ideation stage to scalable products, across different industry verticals. He has lead teams at global companies like Microsoft and IBM Research / IBM Japan, along with national research labs like RIKEN Japan and the Max Planck Institute Germany. Previously, he helped establish and lead the technology division at lean startups in Japan and Singapore, in semiconductor technology, robotics and Fintech sectors. Sakya is the inventor of over 20 patents and has published widely on machine learning and AI with over 1,000 citations.
Sakya holds a PhD. in Physics of Complex Systems from the Max Planck Institute in Germany, along with Masters in Artificial Intelligence from The University of Edinburgh and a Bachelors of Computer Engineering. Prior to founding EdgeCortix he completed his entrepreneurship studies from the MIT Sloan School of Management.
Sailesh Chittipeddi
Dr. Sailesh Chittipeddi became the Executive Vice President and the General Manager of the Embedded Processing, Digital Power and Signal Chain Solutions Group of Renesas in July 2019. He joined Renesas in March 2019.
Before joining Renesas, he served as IDT’s Executive Vice President of Global Operations and CTO, with an additional focus on corporate growth and differentiation. In this role, he was responsible for the company’s operations, procurement, quality, supply chain, foundry engineering, assembly engineering, product & test engineering, facilities, Design Automation, and Information Technology groups. From a product line perspective, he had responsibility for the IoT Systems Group, RapidWave Interconnect Systems, PCIe and Standard Products Group. Additionally, Dr. Chittipeddi helped IDT leverage its existing strengths to increase corporate value while driving the rapid delivery of new products.
Prior to joining IDT, Dr. Chittipeddi was President and CEO of Conexant Systems and served on its Board of Directors. He led the company in its transition from a public company to private ownership and through its debt restructuring efforts. Before that, he held several executive roles at Conexant Systems, including COO, co-President, EVP for Operations and Chief Technical Officer, with responsibility for global engineering, product development, operations, quality, facilities, IT and associated infrastructure support. Dr. Chittipeddi started his career in technology with AT&T Bell Labs and progressively managed larger engineering and operations groups with AT&T Microelectronics/Lucent and Agere Systems. Dr. Chittipeddi serves on the Board of Directors for Avalanche Technology (USA) and Tessolve (Division of Hero Electronix, India). He also serves as a Board Observer in Blu Wireless Technology (Bristol, UK), Peraso Technologies (Canada) and Anagog (Israel).
Dr. Chittipeddi holds five degrees, including an MBA from the University of Texas at Austin and a Ph.D. in physics from The Ohio State University. Dr. Chittipeddi has earned 64 U.S. patents related to semiconductor process, package and design, and has had nearly 40 technical articles published.
Real-time personalized recommendations (RTPRec) have become increasingly prevalent in the digital realm, particularly as more users have become accustomed to using mobile apps and consuming larger amounts of digital data, videos, and engaging in e-commerce activities online following the Covid-19 pandemic.
DL-based recommender is known for its superior accuracy in handling unstructured data, often referred to as embeddings. This characteristic makes them ideal candidates for personalized recommendations. However, it's important to note that DL-based models can involve an extensive number of parameters, ranging into the billions or even trillions, which can pose significant challenges when real-time processing is crucial.
To address this challenge, various strategies such as inference optimization, model compression, and the utilization of hardware accelerators have been introduced to enhance performance and meet the stringent latency requirements of real-time applications. Additionally, this session will delve into accelerator-based distributed systems, offering insights into memory management and performance scalability from an infrastructure perspective.
CL Chen
CL Chen is an accomplished leader in the IC design industry with a remarkable career spanning over 27 years including CTO, AMTC Corp., Programme Manager TSMC, Director at Global Unichip Corp which is a TSMC subsidiary public company specialized in SOC design service. His wealth of experience and expertise has contributed significantly to the growth and innovation of the field.
As the Chief Operating Officer of NEUCHIPS, he continues to drive excellence and foster partnerships within the industry. He possesses a wealth of experience in domain specific inferencing accelerator, particularly within the burgeoning field of e-commerce. His insights and contributions have enhanced the customer experience within Taiwan's e-commerce sector, underscoring his commitment to leveraging technology for real-world impact. At NEUCHIPS, CL's role as COO signifies his dedication to advancing the company's operations, growth, and strategic partnerships. His extensive network within the industry, coupled with his proven track record of connecting eco partners, has been instrumental in propelling NEUCHIPS to new heights.
Puja Das
Dr. Puja Das, leads the Personalization team at Warner Brothers Discovery (WBD) which includes offerings on Max, HBO, Discovery+ and many more.
Prior to WBD, she led a team of Applied ML researchers at Apple, who focused on building large scale recommendation systems to serve personalized content on the App Store, Arcade and Apple Books. Her areas of expertise include user modeling, content modeling, recommendation systems, multi-task learning, sequential learning and online convex optimization. She also led the Ads prediction team at Twitter (now X), where she focused on relevance modeling to improve App Ads personalization and monetization across all of Twitter surfaces.
She obtained her Ph.D from University of Minnesota in Machine Learning, where the focus of her dissertation was online learning algorithms, which work on streaming data. Her dissertation was the recipient of the prestigious IBM Ph D. Fellowship Award.
She is active in the research community and part of the program committee at ML and recommendation system conferences. Shas mentored several undergrad and grad students and participated in various round table discussions through Grace Hopper Conference, Women in Machine Learning Program colocated with NeurIPS, AAAI and Computing Research Association- Women’s chapter.
Xinghai Hu
Xinghai Hu is currently the head of TikTok US recommendation team. His team works on responsible recommendation system, improving general safety and trustability of content recommendations.
Anlu Xing
Anlu Xing is a Senior Research Scientist/Machine Learning Engineer at Meta, working on LLM applications for business product (GenAI for Monetization)
and leading projects on the company's top priority product -- Short-form video (reels) recommendation, ranking and creator relevance.
Julius Lo
Julius is a Director of NEUCHIPS, an AI ASIC startup for recommendation inferencing. Julius leads NEUCHIPS software team, covering server integration to on-board firmware. Before NEUCHIPS, Julius worked for Global Unchip Corp., hTC and Mediatek, devoting himself to RTL circuit design, Linux device drivers and performance optimization with power balancing in 20+ chips. He is an author of 6+ international patents in the areas of scheduling and data compression.
Conway's Law suggests software architecture shapes the structures and dynamics of the organization that produced it. In this panel we will dive into the collaborative process of bringing AI from experimental POCs to real-world applications, involving data scientists, DevOps/MLOps engineers, frontend engineers, and product managers. This session explores challenges spanning infrastructure, model accuracy, user interface design, and business alignment, highlighting successful strategies and fostering interdisciplinary communication. Together, we will try to uncover insights that enhance AI implementation, resulting in technically sound solutions that align with user needs and business goals.
Uri Rosenberg
Uri Rosenberg is the Specialist Technical Manager of AI & ML services within enterprise support at Amazon Web Services (AWS) EMEA. Uri works to empower enterprise customers on all things ML: from underwater computer vision models that monitor fish to training models on satellite images in space; from optimizing costs to strategic discussions on deep learning and ethics. Uri brings his extensive experience to drive success of customers at all stages of ML adoption.
Before AWS, Uri led the ML projects at AT&T innovation center in Israel, working on deep learning models with extreme security and privacy constraints.
Uri is also an AWS certified Lead Machine learning subject matter expert and holds an MsC in Computer Science from Tel-Aviv Academic College, where his research focused on large scale deep learning models.
Lior Khermosh
Lior is passionate and experienced in ML, AI, DNNs and MLops.
Lior was co-founder and Chief Scientist of ParallelM, a leader ML-Ops company.
Prior to that he held the distinguished Fellow role at PMC Sierra and was on the founding team of Passave, a FTTH silicon company.
He holds MSEE & BSEE degrees from Tel Aviv university, both Cum Laude.
Nikhil Gulati
Jaya Kawale
Jaya Kawale is the head of Machine Learning at Tubi, a Fox Corporation content platform. Jaya´s team works on solving various ML problems for Tubi´s product, ranging from recommendations, content understanding and acquisition, ads ML, etc. Her team also work on the application of cutting edge machine learning technologies such as contextual bandits, deep learning, computer vision and NLP to improve user experience at Tubi.
Nyakio Grieco
Arun Iyengar
Arun Iyengar is the CEO of Untether AI. He brings to Untether AI extensive operational and general management experience across a variety of markets from automotive, cloud, to wired and wireless infrastructure. Prior to Untether AI, Iyengar held leadership roles at Xilinx, AMD, and Altera where he set and executed strategies to grow revenues in each of the targeted markets.
