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Strategy

Strategy

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AI Hardware Summit attendees are invited to attend the an extended networking session where they can meet attendees from across both events. The Meet & Greet is a perfect opportunity to reconnect with peers, expand your network, and discuss the state of ML across the cloud-edge continuum!

Chip Design
Developer Efficiency
Edge AI
Enterprise AI
ML at Scale
NLP
Novel AI Hardware
Systems Design
Data Science
Hardware Engineering
Software Engineering
Strategy
Systems Engineering

Author:

Colin Murdoch

Chief Business Officer
DeepMind

Decades of international commercial experience and deep technical expertise mean Colin is uniquely placed to ensure DeepMind’s cutting-edge research benefits as many people as possible. As Chief Business Officer of DeepMind, he oversees a wide-range of teams including Applied, which applies research breakthroughs to Google products and infrastructure used by billions of people. He also helps drive the growth of DeepMind, building and leading critical functions including finance and strategy and leading external and commercial partnerships. Originally an electronics and software engineer, he has held senior positions at both start-ups and global companies such as Thomson Reuters, helping them solve their own complex, mission-critical, real-world challenges.

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Colin Murdoch

Chief Business Officer
DeepMind

Decades of international commercial experience and deep technical expertise mean Colin is uniquely placed to ensure DeepMind’s cutting-edge research benefits as many people as possible. As Chief Business Officer of DeepMind, he oversees a wide-range of teams including Applied, which applies research breakthroughs to Google products and infrastructure used by billions of people. He also helps drive the growth of DeepMind, building and leading critical functions including finance and strategy and leading external and commercial partnerships. Originally an electronics and software engineer, he has held senior positions at both start-ups and global companies such as Thomson Reuters, helping them solve their own complex, mission-critical, real-world challenges.

Author:

Cade Metz

Technology Correspondent
New York Times

Cade Metz is a reporter with The New York Times, covering artificial intelligence, driverless cars, robotics, virtual reality, and other emerging areas. Genius Makers is his first book. Previously, he was a senior staff writer with Wired magazine and the U.S. editor of The Register, one of Britain’s leading science and technology news sites.

A native of North Carolina and a graduate of Duke University, Metz, 48, works in The New York Times’ San Francisco bureau and lives across the bay with his wife Taylor and two daughters.

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Cade Metz

Technology Correspondent
New York Times

Cade Metz is a reporter with The New York Times, covering artificial intelligence, driverless cars, robotics, virtual reality, and other emerging areas. Genius Makers is his first book. Previously, he was a senior staff writer with Wired magazine and the U.S. editor of The Register, one of Britain’s leading science and technology news sites.

A native of North Carolina and a graduate of Duke University, Metz, 48, works in The New York Times’ San Francisco bureau and lives across the bay with his wife Taylor and two daughters.

Edge AI
Enterprise AI
ML at Scale
Systems Design
Data Science
Software Engineering
Strategy
Systems Engineering

Author:

Vinesh Sukumar

Head of AI Product Management
Qualcomm

Vinesh Sukumar currently serves as Senior Director – Head of AI/ML product management at Qualcomm Technologies, Inc (QTI).  In this role, he leads AI product definition, strategy and solution deployment across multiple business units.

•He has about 20 years of industry experience spread across research, engineering and application deployment. He currently holds a doctorate degree specializing in imaging and vision systems while also completing a business degree focused on strategy and marketing. He is a regular speaker in many AI industry forums and has authored several journal papers and two technical books.

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Vinesh Sukumar

Head of AI Product Management
Qualcomm

Vinesh Sukumar currently serves as Senior Director – Head of AI/ML product management at Qualcomm Technologies, Inc (QTI).  In this role, he leads AI product definition, strategy and solution deployment across multiple business units.

•He has about 20 years of industry experience spread across research, engineering and application deployment. He currently holds a doctorate degree specializing in imaging and vision systems while also completing a business degree focused on strategy and marketing. He is a regular speaker in many AI industry forums and has authored several journal papers and two technical books.

Author:

Barrie Mullins

VP, Product
Flex Logix

Barrie has 25+ years of experience working with edge, embedded and AI systems across multiple industries including industrial, automotive, robotics, storage, and communications. Previously, he spent a year at Blaize as head of marketing, and three years at NVIDIA where he led the Jetson Product Marketing team. Prior to NVIDIA, he held multiple roles in Xilinx, including leading product marketing and management for the Zynq product line, sales enablement, business development, customer program management and managing design services. Barrie moved to the United States in 2007 from Ireland, where he worked for Xilinx and two starts ups, Raidtec Corp. and Eurologic Systems, in the Data Storage space where he holds three patents.  

Barrie received his EE from the Munster Technological University, an ME from University College Dublin and an MBA from Santa Clara University’s Leavey School of Business. 

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Barrie Mullins

VP, Product
Flex Logix

Barrie has 25+ years of experience working with edge, embedded and AI systems across multiple industries including industrial, automotive, robotics, storage, and communications. Previously, he spent a year at Blaize as head of marketing, and three years at NVIDIA where he led the Jetson Product Marketing team. Prior to NVIDIA, he held multiple roles in Xilinx, including leading product marketing and management for the Zynq product line, sales enablement, business development, customer program management and managing design services. Barrie moved to the United States in 2007 from Ireland, where he worked for Xilinx and two starts ups, Raidtec Corp. and Eurologic Systems, in the Data Storage space where he holds three patents.  

Barrie received his EE from the Munster Technological University, an ME from University College Dublin and an MBA from Santa Clara University’s Leavey School of Business. 

Author:

Vinay Palakkode

Senior Staff ML Engineer & Manager
Rivian

“Vinay Palakkode is a senior staff machine learning engineer and manages a team of deep learning researchers and engineers at Rivian Automotive’s self-driving organization. Vinay holds a master’s degree in electrical and computer engineering from Carnegie Mellon University. He specializes in perception for robotics and high-performance computing. Vinay held prior engineering and management positions at Apple’s Technology Development Group (TDG) and Special Projects Groups (SPG).”

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Vinay Palakkode

Senior Staff ML Engineer & Manager
Rivian

“Vinay Palakkode is a senior staff machine learning engineer and manages a team of deep learning researchers and engineers at Rivian Automotive’s self-driving organization. Vinay holds a master’s degree in electrical and computer engineering from Carnegie Mellon University. He specializes in perception for robotics and high-performance computing. Vinay held prior engineering and management positions at Apple’s Technology Development Group (TDG) and Special Projects Groups (SPG).”

Author:

Vamsi Nalluri

Machine Learning HW Architect
Rivian

Vamsi is ML HW Architect at Rivian, and has 17 years of experience in the semiconductor industry working on architecture, verification, and validation.


He most recently was at Xilinx, where he has accelerated sparse neural networks to achieve 3X hardware performance improvement on the 7nm flagship technology platform from Xilinx on many of the industry standard networks like ResNetv50, Yolo and other CNN benchmarks.

Prior to that, he has architected and trained dataflow implementations of quantized and mixed precision neural networks at Intel. 

He graduated from IIT Madras with a B.Tech in Electrical Engineering and is a big tennis fan - which includes playing and watching

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Vamsi Nalluri

Machine Learning HW Architect
Rivian

Vamsi is ML HW Architect at Rivian, and has 17 years of experience in the semiconductor industry working on architecture, verification, and validation.


He most recently was at Xilinx, where he has accelerated sparse neural networks to achieve 3X hardware performance improvement on the 7nm flagship technology platform from Xilinx on many of the industry standard networks like ResNetv50, Yolo and other CNN benchmarks.

Prior to that, he has architected and trained dataflow implementations of quantized and mixed precision neural networks at Intel. 

He graduated from IIT Madras with a B.Tech in Electrical Engineering and is a big tennis fan - which includes playing and watching

Author:

Hui Wang

Machine Learning Engineer
Schlumberger
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Hui Wang

Machine Learning Engineer
Schlumberger

RISC-V adoption has increased dramatically throughout 2022, due to the architecture's simple intruction set, the ability to better pre-process NNs for acceleration and its open source nature. The advent of the RISC-V vector extension allows AI processor builders to develop on top of instructions that other companies are using and then innovate in whatever domain they want to specialize in.


Chip Design
Edge AI
ML at Scale
Novel
Systems Design
Hardware Engineering
Strategy
Systems Engineering

Author:

Bing Yu

Senior Technical Director
Andes Technology

Bing Yu is a Sr. Technical Director at Andes Technology. He has over 30 years of experience in technical leadership and management, specializing in machine learning hardware, high performance CPUs and system architecture. In his current role, he is responsible for processor roadmap, architecture, and product design. Bing received his BS degree in Electrical Engineering from San Jose State University and completed the Stanford Executive Program (SEP) at the Stanford Graduate School of Business.

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Bing Yu

Senior Technical Director
Andes Technology

Bing Yu is a Sr. Technical Director at Andes Technology. He has over 30 years of experience in technical leadership and management, specializing in machine learning hardware, high performance CPUs and system architecture. In his current role, he is responsible for processor roadmap, architecture, and product design. Bing received his BS degree in Electrical Engineering from San Jose State University and completed the Stanford Executive Program (SEP) at the Stanford Graduate School of Business.

Cerebras Systems builds the fastest AI accelerators in the industry. In this talk we will review how the size and scope of massive natural language processing (NLP) presents fundamental challenges to legacy compute and to traditional cloud providers. We will explore the importance of guaranteed node to node latency in large clusters, how that can’t be achieved in the cloud, and how it prevents linear and even deterministic scaling. We will examine the complexity of distributing NLP models over hundreds or thousands of GPUs and show how quickly and easily a cluster of Cerebras CS-2s is set up, and how linear scaling can be achieved over millions of compute cores with Cerebras technology. And finally, we will show how innovative customers are using clusters of Cerebras CS-2s to train large language models in order to solve both basic and applied scientific challenges, including understanding the COVID-19 replication mechanism, epigenetic language modelling for drug discovery, and in the development of clean energy. This enables researchers to test ideas that may otherwise languish for lack of resources and, ultimately, reduces the cost of curiosity.  ​

 

Chip Design
Enterprise AI
ML at Scale
Novel AI Hardware
Systems Design
Data Science
Hardware Engineering
Software Engineering
Strategy
Systems Engineering

Author:

Andy Hock

VP, Product Management
Cerebras

Dr. Andy Hock is VP of Product Management at Cerebras Systems with responsibility for product strategy. His organization drives engagement with engineering and our customers to inform the hardware, software, and machine learning technical requirements and accelerate world-leading AI with Cerebras’ products. Prior to Cerebras, Andy has held senior leadership positions with Arete Associates, Skybox Imaging (acquired by Google), and Google. He holds a PhD in Geophysics and Space Physics from UCLA.

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Andy Hock

VP, Product Management
Cerebras

Dr. Andy Hock is VP of Product Management at Cerebras Systems with responsibility for product strategy. His organization drives engagement with engineering and our customers to inform the hardware, software, and machine learning technical requirements and accelerate world-leading AI with Cerebras’ products. Prior to Cerebras, Andy has held senior leadership positions with Arete Associates, Skybox Imaging (acquired by Google), and Google. He holds a PhD in Geophysics and Space Physics from UCLA.

This presentation, by the RISC-V founder, will highlight how RISC-V and vector compute are gaining momentum with AI and ML and computer vision and how it addresses challenges like managing power consumption, extra data movement, the need for multiple libraries, and issues with generational incompatibility. To solve these obstacles, many of the world’s largest data and device companies are turning to vector processing based on the RISC‑V Vector (RVV) 1.0 ISA.

Chip Design
Enterprise AI
ML at Scale
Novel AI Hardware
Hardware Engineering
Systems Engineering
Strategy

Author:

Krste Asanovic

Co-Founder & Chief Architect
SiFive

Krste is SiFive’s Chief Architect and a Co-Founder. He is also a Professor in the EECS Department at the University of California, Berkeley, where he also serves as Director of the ADEPT Lab. Krste leads the RISC‑V ISA project at Berkeley and is Chairman of the RISC‑V Foundation. He is an ACM Fellow and an IEEE Fellow. Krste received his PhD from UC Berkeley, and a BA in Electrical and Information Sciences from the University of Cambridge.

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Krste Asanovic

Co-Founder & Chief Architect
SiFive

Krste is SiFive’s Chief Architect and a Co-Founder. He is also a Professor in the EECS Department at the University of California, Berkeley, where he also serves as Director of the ADEPT Lab. Krste leads the RISC‑V ISA project at Berkeley and is Chairman of the RISC‑V Foundation. He is an ACM Fellow and an IEEE Fellow. Krste received his PhD from UC Berkeley, and a BA in Electrical and Information Sciences from the University of Cambridge.

As customer success stories from AI accelerator start ups starting to proliferate, and traction starting to ramp up, it is starting to become clear which ML workloads are most amenable to domain specific architectures, and which market sectors are most likely to adopt novel AI acceleration technologies. 

With one company still retaining the majority of market share in the datacenter, and the edge currently a complete wilderness, it might still be a difficult time to launch a new accelerator company. But opportunities for capturing market share across the cloud-edge continuum definitely exist! In the world of HPC, certain ML and non-ML scientific workloads have seen extraordinary, demonstrable speed ups on novel ML systems architectures, and the scientific community only sees demand for acceleration of these types of workloads growing. At the edge some AI chip companies are already shipping in volume, while new applications emerge continuously.

This panel will look at what it takes to make it in the AIHW game, what might shift the balance of power in the datacenter, and how companies can find a niche at the edge. 

Enterprise AI
Novel AI Hardware
Strategy
Industry & Investment

Author:

Brett Simpson

Co-Founder & Senior Analyst
Arete Research

Brett is a co-founder of Arete (formed in 2000) and is based in the firm's London office. He focuses on the global semiconductor component sector. Brett is a regular public speaker at industry events and after 17 years looking at the sector, has a wealth of experience to draw on. Prior to Arete, Brett spent two years at Goldman Sachs in an equity analyst role, specialising in European technology following three years with Ericsson UK, working in business development, covering all aspects of wireline and wireless telecom infrastructure.

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Brett Simpson

Co-Founder & Senior Analyst
Arete Research

Brett is a co-founder of Arete (formed in 2000) and is based in the firm's London office. He focuses on the global semiconductor component sector. Brett is a regular public speaker at industry events and after 17 years looking at the sector, has a wealth of experience to draw on. Prior to Arete, Brett spent two years at Goldman Sachs in an equity analyst role, specialising in European technology following three years with Ericsson UK, working in business development, covering all aspects of wireline and wireless telecom infrastructure.

Author:

Gayathri Radhakrishnan

Partner
Hitachi Ventures

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.

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Gayathri Radhakrishnan

Partner
Hitachi Ventures

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.

Author:

Karthee Madasamy

Founder & Managing Partner
MFV Partners
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Karthee Madasamy

Founder & Managing Partner
MFV Partners

Author:

Samir Kumar

GM & Managing Director
M12
Samir is a managing director at M12, leading investments globally where artificial intelligence or machine learning is a key point of leverage.
He also stewards the fund’s Vanguard Bets investment category—startups aiming for breakthroughs that will result in generational shifts in the technology landscape. Samir’s other investment focuses include quantum computing, robotics, autonomous systems, transportation and silicon—especially for AI. Samir manages a team developing theses for new technology areas and oversees the fund’s technical and scientific advisory board.
Prior to joining M12, Samir was a senior director of business development and product management in Qualcomm’s corporate R&D division. There, he led early-stage product validation, partnerships, acquisitions, and strategy for embedded machine learning, computer vision and heterogeneous computing. Samir started his career at Microsoft, where he spent several years leading product management and product planning efforts for enterprise mobility before joining Palm and Samsung.
Samir is a regular conference speaker on his investment focus areas. He has served on or moderated panels of VCs and subject matter experts at LDV Vision Summit, tinyML Summit, and Cybersec&AI Connected.
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Samir Kumar

GM & Managing Director
M12
Samir is a managing director at M12, leading investments globally where artificial intelligence or machine learning is a key point of leverage.
He also stewards the fund’s Vanguard Bets investment category—startups aiming for breakthroughs that will result in generational shifts in the technology landscape. Samir’s other investment focuses include quantum computing, robotics, autonomous systems, transportation and silicon—especially for AI. Samir manages a team developing theses for new technology areas and oversees the fund’s technical and scientific advisory board.
Prior to joining M12, Samir was a senior director of business development and product management in Qualcomm’s corporate R&D division. There, he led early-stage product validation, partnerships, acquisitions, and strategy for embedded machine learning, computer vision and heterogeneous computing. Samir started his career at Microsoft, where he spent several years leading product management and product planning efforts for enterprise mobility before joining Palm and Samsung.
Samir is a regular conference speaker on his investment focus areas. He has served on or moderated panels of VCs and subject matter experts at LDV Vision Summit, tinyML Summit, and Cybersec&AI Connected.

As AI makes its way into healthcare and medical applications, the role of hardware accelerators in the successful deployment of such large AI models becomes more and more important. Nowadays large language models, such as GPT-3 and T5, offer unprecedented opportunities to solve challenging healthcare business problems like drug discovery, medical term mapping and insight generation from electronic health records. However, efficient and cost effective training, as well as deployment and maintenance of such models in production remains a challenge for healthcare industry. This presentation will review a few open challenges and opportunities in the healthcare industry and the benefits that AI hardware innovation may bring to the ML utilization.

Developer Efficiency
Enterprise AI
ML at Scale
NLP
Novel AI Hardware
Systems Design
Data Science
Software Engineering
Strategy
Systems Engineering

Author:

Hooman Sedghamiz

Senior Director of AI & ML
Bayer

Hooman Sedghamiz is Director of AI & ML at Bayer. He has lead algorithm development and generated valuable insights to improve medical products ranging from implantable, wearable medical and imaging devices to bioinformatics and pharmaceutical products for a variety of multinational medical companies.

He has lead projects, data science teams and developed algorithms for closed loop active medical implants (e.g. Pacemakers, cochlear and retinal implants) as well as advanced computational biology to study the time evolution of cellular networks associated with cancer , depression and other illnesses.

His experience in healthcare also extends to image processing for Computer Tomography (CT), iX-Ray (Interventional X-Ray) as well as signal processing of physiological signals such as ECG, EMG, EEG and ACC.

Recently, his team has been working on cutting edge natural language processing and developed cutting edge models to address the healthcare challenges dealing with textual data.

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Hooman Sedghamiz

Senior Director of AI & ML
Bayer

Hooman Sedghamiz is Director of AI & ML at Bayer. He has lead algorithm development and generated valuable insights to improve medical products ranging from implantable, wearable medical and imaging devices to bioinformatics and pharmaceutical products for a variety of multinational medical companies.

He has lead projects, data science teams and developed algorithms for closed loop active medical implants (e.g. Pacemakers, cochlear and retinal implants) as well as advanced computational biology to study the time evolution of cellular networks associated with cancer , depression and other illnesses.

His experience in healthcare also extends to image processing for Computer Tomography (CT), iX-Ray (Interventional X-Ray) as well as signal processing of physiological signals such as ECG, EMG, EEG and ACC.

Recently, his team has been working on cutting edge natural language processing and developed cutting edge models to address the healthcare challenges dealing with textual data.

Chip Design
ML at Scale
Novel AI Hardware
Systems Design
Hardware Engineering
Strategy
Systems Engineering

Author:

Nitza Basoco

VP, Business Development
proteanTecs

Nitza Basoco is a technology leader with over 20 years of semiconductor experience. At proteanTecs, she leads the Business Development team, responsible for driving partnership strategies and building value-add ecosystem growth. 

Previously, Nitza was the VP of Operations at Synaptics with responsibility for growing and scaling their worldwide test development, product engineering and manufacturing departments. Prior to Synaptics, Nitza spent a decade holding various leadership positions within the operations organization at MaxLinear, ranging from test development engineering to supply chain. Earlier in her career, Nitza served as a Principal Test Development Engineer for Broadcom Corporation and as a Broadband Applications Engineer at Teradyne.  

Nitza holds MEng and BSEE degrees from Massachusetts Institute of Technology.

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Nitza Basoco

VP, Business Development
proteanTecs

Nitza Basoco is a technology leader with over 20 years of semiconductor experience. At proteanTecs, she leads the Business Development team, responsible for driving partnership strategies and building value-add ecosystem growth. 

Previously, Nitza was the VP of Operations at Synaptics with responsibility for growing and scaling their worldwide test development, product engineering and manufacturing departments. Prior to Synaptics, Nitza spent a decade holding various leadership positions within the operations organization at MaxLinear, ranging from test development engineering to supply chain. Earlier in her career, Nitza served as a Principal Test Development Engineer for Broadcom Corporation and as a Broadband Applications Engineer at Teradyne.  

Nitza holds MEng and BSEE degrees from Massachusetts Institute of Technology.

Author:

Judy Priest

Distinguished Engineer & VP, GM
Microsoft

Judy Priest is a Distinguished Engineer in Microsoft's Cloud and AI Group. She drives innovation, integration, and operations in next generation Data Center platforms supporting Azure, AI, and MS's Enterprise software. Judy has over 25 years of experience in developing data centers systems and silicon, high speed signaling technologies and optics, circuit design, and physical architectures for compute, storage, graphics, and networking.

Judy has previously worked at Cisco Systems, Silicon Graphics, Hewlett-Packard, and Digital Equipment Corporation, as well as two startup ventures. She serves on the Board of Directors for Women's Audio Mission, a local SF nonprofit moving the needle for girls, women, and GNC individuals in STEM through music. Judy was awarded Business Insider's 2018 Most Powerful Female Engineers and InterCon Networking's 2020 Top 100 Leaders in Engineering.

 

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Judy Priest

Distinguished Engineer & VP, GM
Microsoft

Judy Priest is a Distinguished Engineer in Microsoft's Cloud and AI Group. She drives innovation, integration, and operations in next generation Data Center platforms supporting Azure, AI, and MS's Enterprise software. Judy has over 25 years of experience in developing data centers systems and silicon, high speed signaling technologies and optics, circuit design, and physical architectures for compute, storage, graphics, and networking.

Judy has previously worked at Cisco Systems, Silicon Graphics, Hewlett-Packard, and Digital Equipment Corporation, as well as two startup ventures. She serves on the Board of Directors for Women's Audio Mission, a local SF nonprofit moving the needle for girls, women, and GNC individuals in STEM through music. Judy was awarded Business Insider's 2018 Most Powerful Female Engineers and InterCon Networking's 2020 Top 100 Leaders in Engineering.

 

Author:

Shivam Bharuka

Software Production Engineer
Meta

Shivam is an engineering leader with Meta as part of the AI Infrastructure team for the last three years. During this time, he has helped scale the machine learning training infrastructure at Meta to support large scale ranking and recommendation models, serving more than a billion users. He is responsible for driving performance, reliability, and efficiency-oriented designs across the components of the ML training stack at Meta. Shivam holds a B.S. and an M.S. in Computer Engineering from the University of Illinois at Urbana-Champaign.

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Shivam Bharuka

Software Production Engineer
Meta

Shivam is an engineering leader with Meta as part of the AI Infrastructure team for the last three years. During this time, he has helped scale the machine learning training infrastructure at Meta to support large scale ranking and recommendation models, serving more than a billion users. He is responsible for driving performance, reliability, and efficiency-oriented designs across the components of the ML training stack at Meta. Shivam holds a B.S. and an M.S. in Computer Engineering from the University of Illinois at Urbana-Champaign.

Author:

Jim von Bergen

Senior Director, Product Quality Engineering
Cisco
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Jim von Bergen

Senior Director, Product Quality Engineering
Cisco