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Edge AI is going to play a significant role in many areas such as automotive, smart home, smart cities, education, robotics, and surveillance, to name a few. The past few years have seen a rise in the number of HW options designed for accelerating AI inference at the edge. These multiple HW options, however, have made application development for the edge complicated. Each HW option comes with its own inference runtime, model porting SW, operator support, optimizations, and model zoo making it  a time consuming effort to evaluate the HW. Performance metrics are not standardized across HW options and even for a fixed HW, they vary depending on the model and the host system. All these factors make evaluating edge HW a challenging task. In this talk, we will provide an overview of these challenges as well as our attempts to alleviate these problems.

On Device ML
Innovation at the Edge
Chip and Systems Design
Edge Trade Offs
Hardware and Systems Engineering

Author:

Shashi Kiran Chilappagari

Co-Founder and Chief Architect
DeGirum Corporation

Shashi Chilappagari is the Co-Founder and Chief Architect at DeGirum Corp., a fabless semiconductor company building complete AI solutions for the edge. Prior to DeGirum, he was the Director of SSD Architecture at Marvell Semiconductor Inc. Shashi has B. Tech and M. Tech degrees from Indian Institute of Technology, Madras, India and Ph.D. from the University of Arizona, Tucson, Arizona.

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Shashi Kiran Chilappagari

Co-Founder and Chief Architect
DeGirum Corporation

Shashi Chilappagari is the Co-Founder and Chief Architect at DeGirum Corp., a fabless semiconductor company building complete AI solutions for the edge. Prior to DeGirum, he was the Director of SSD Architecture at Marvell Semiconductor Inc. Shashi has B. Tech and M. Tech degrees from Indian Institute of Technology, Madras, India and Ph.D. from the University of Arizona, Tucson, Arizona.

Designing hardware architectures for artificial intelligence acceleration (AI) at the edge is hard, especially for achieving the combination of high energy-efficiency and performance.  The reason Edge AI is so difficult today, is that it’s really a systems challenge, with a heterogenous mixture of software, hardware processors and diverse applications.  Codesigning a robust sy stem software stack that works across such heterogeneity, together with an efficient and scalable processor architecture is often neglected and poses a bigger challenge. 

In this talk we will describe howEdgeCortix is bridging this software & hardware divide, going beyond theoretical performance metrics, with the combination of a robust compiler, scheduler, runtime engine, and a reconfigurable co-processor. Using edge AI use-case examples, this presentation will describe how the combination of our MERA™ software (framework) and first-generation SAKURA-AI™ co-processor  achieves high efficiency, while also effectively hiding the hardware complexity, delivering a compelling solution tailored for the needs of todays edge AI application developers.

On Device ML
Innovation at the Edge
Chip and Systems Design
Edge Trade Offs

Author:

Sakyasingha Dasgupta

Founder & CEO
EdgeCortix

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.

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Sakyasingha Dasgupta

Founder & CEO
EdgeCortix

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.

Author:

Ingolf Held

CEO
GrAI Matter Labs

Mr. Ingolf Held has been Chief Executive Officer of GrAI Matter Labs (GML) since January 2018. Prior to GML, he was responsible for technology strategy and product marketing of imaging and computer vision at Intel. He holds an MScEE from Erlangen University and an MBA from Rotterdam School of Management.

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Ingolf Held

CEO
GrAI Matter Labs

Mr. Ingolf Held has been Chief Executive Officer of GrAI Matter Labs (GML) since January 2018. Prior to GML, he was responsible for technology strategy and product marketing of imaging and computer vision at Intel. He holds an MScEE from Erlangen University and an MBA from Rotterdam School of Management.

Qualcomm is leading the realization of the Connected Intelligent Edge, where the convergence of wireless connectivity, efficient computing and distributed AI will power the devices and experiences that you deserve. In this talk, we’ll explore how Qualcomm is deploying on device AI across diverse edge products in markets including mobile, automotive, XR, IoT, robotics and PCs. We will also explore our recently announced Qualcomm AI Stack, a comprehensive AI software solution for OEMs and developers. For the first time, a single AI software portfolio works on every Qualcomm Technologies platform spanning the wide range of Connected Intelligent Edge products.

Author:

Ziad Asghar

Vice President, Snapdragon Roadmap Planning and AI, XR & Competitive Strategy
Qualcomm

Ziad Asghar is Vice President, Product Management at Qualcomm Technologies, Inc (QTI).  He leads Snapdragon roadmap planning and Application processor technologies, covering all QC product lines.  Ziad drives the definition of the products ensuring that our products lead in technology and enable best in class user experiences while making tradeoffs between features, power, performance and cost.  He also leads Application Processor technologies including Artificial Intelligence, Camera, Graphics, CPU, Audio, Video and Security.  He also has responsibility for Competitive Analysis.  Ziad works across all teams including engineering and product management to ensure that we have the leading roadmap in the industry and continue to set the standard on all application processor technologies.  He works across all business units including Mobile, Automotive, Compute, XR, Edge Cloud and IoT.

He has more than 20 years of experience in the wireless semiconductor industry where he has held a broad set of leadership positions from R&D to product management.  Prior to joining Qualcomm, Ziad was at Texas Instruments where he worked on systems design of UMTS & LTE and OMAP Product Management.  

Ziad holds an MBA from UCSD and master’s degrees in electrical engineering from Purdue University and Southern Methodist University.

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Ziad Asghar

Vice President, Snapdragon Roadmap Planning and AI, XR & Competitive Strategy
Qualcomm

Ziad Asghar is Vice President, Product Management at Qualcomm Technologies, Inc (QTI).  He leads Snapdragon roadmap planning and Application processor technologies, covering all QC product lines.  Ziad drives the definition of the products ensuring that our products lead in technology and enable best in class user experiences while making tradeoffs between features, power, performance and cost.  He also leads Application Processor technologies including Artificial Intelligence, Camera, Graphics, CPU, Audio, Video and Security.  He also has responsibility for Competitive Analysis.  Ziad works across all teams including engineering and product management to ensure that we have the leading roadmap in the industry and continue to set the standard on all application processor technologies.  He works across all business units including Mobile, Automotive, Compute, XR, Edge Cloud and IoT.

He has more than 20 years of experience in the wireless semiconductor industry where he has held a broad set of leadership positions from R&D to product management.  Prior to joining Qualcomm, Ziad was at Texas Instruments where he worked on systems design of UMTS & LTE and OMAP Product Management.  

Ziad holds an MBA from UCSD and master’s degrees in electrical engineering from Purdue University and Southern Methodist University.

Spiking neural networks are central to the brain’s processing of sensory data, and are key to its ability to recognize spatial and time-series patterns quickly and efficiently.

Innatera’s Spiking Neural Processor (SNP) enables always-on AI applications based on programmable, energy-efficient acceleration of spiking neural networks. It achieves unprecedented AI-based functions at the sensor-edge within a sub-milliwatt power and sub-millisecond latency envelope, using spiking models 100x smaller than conventional neural networks. In this talk, CEO Dr. Sumeet Kumar explores the capabilities of the SNP, and how these link with the critical requirements of sensing use-cases in the consumer and industrial domains. Further, Dr. Kumar provides insight into Innatera’s Talamo software development kit which brings the power and familiarity of PyTorch to spiking neural networks.

Author:

Sumeet Kumar

CEO
Innatera Nanosystems

Dr. Sumeet Kumar is CEO of Innatera Nanosystems, the pioneering Dutch neuromorphic processor company. Dr. Kumar holds an MSc and PhD in Microelectronics from the Delft University of Technology, The Netherlands. He was previously with Intel, where he worked with the Imaging and Camera Technologies Group developing domain-specific tools for the development of complex media processor architectures. At Delft, Dr. Kumar is credited with creating two highly-successful European R&D programmes developing energy-efficient compute hardware for highly automated vehicles, together with organizations including Infineon, NXP, and BMW, among others. He was also responsible for leading industry-focused research on power-efficient multiprocessors and computational neuroscience.

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Sumeet Kumar

CEO
Innatera Nanosystems

Dr. Sumeet Kumar is CEO of Innatera Nanosystems, the pioneering Dutch neuromorphic processor company. Dr. Kumar holds an MSc and PhD in Microelectronics from the Delft University of Technology, The Netherlands. He was previously with Intel, where he worked with the Imaging and Camera Technologies Group developing domain-specific tools for the development of complex media processor architectures. At Delft, Dr. Kumar is credited with creating two highly-successful European R&D programmes developing energy-efficient compute hardware for highly automated vehicles, together with organizations including Infineon, NXP, and BMW, among others. He was also responsible for leading industry-focused research on power-efficient multiprocessors and computational neuroscience.

 

Georgie Kovaks

Founder
Fempower Health

Georgie Kovaks

Founder
Fempower Health

Georgie Kovaks

Founder
Fempower Health

The future of AI begins at the sensor. Join BrainChip for this exploration of relevant data propagation, regions of interest and making the applications of tomorrow more efficient today by processing at the sensor.

On Device ML
Vision
Edge Trade Offs
Software Engineering
Hardware and Systems Engineering

Author:

Denis Gudovskiy

Senior Deep Learning Researcher
Panasonic AI Lab
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Denis Gudovskiy

Senior Deep Learning Researcher
Panasonic AI Lab
 

Vinita Kailasanath

Partner, Life Sciences and Tech Transactions
Freshfields

Vinita Kailasanath

Partner, Life Sciences and Tech Transactions
Freshfields

Vinita Kailasanath

Partner, Life Sciences and Tech Transactions
Freshfields
  • How does computer vision work?
  • Overview of use cases
  • References
  • Short slide of offering
  • Rule Based Engine
  • Alert system or reporting
  • Deployment & implementation strategies
On Device ML
Vision
Software Engineering
Hardware and Systems Engineering

Author:

Anthony Valle

NALA Presales Senior Engineer
ATOS

 

Anthony Valle is a Senior Pre-Sales Engineer for North America and Latin America at Ipsotek, an Atos company. Anthony has over 20 years of experience in IT and security technology solutions for the rapidly growing tech-based world.  He works closely with clients in developing solutions for AI at the Edge, utilizing a patented Scenario-Based Rule Engine (SBRE), a powerful tool to precisely define behaviors of interest as they would unfold in the real-world dynamic and complex environment.

Prior to joining Atos, he performed first Sales Engineering and later Application engineering roles for Avigilon, one of the world's largest security manufacturers.   Throughout his career, he has held key management positions within the industry and sought many certifications to further his career in security technology. 

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Anthony Valle

NALA Presales Senior Engineer
ATOS

 

Anthony Valle is a Senior Pre-Sales Engineer for North America and Latin America at Ipsotek, an Atos company. Anthony has over 20 years of experience in IT and security technology solutions for the rapidly growing tech-based world.  He works closely with clients in developing solutions for AI at the Edge, utilizing a patented Scenario-Based Rule Engine (SBRE), a powerful tool to precisely define behaviors of interest as they would unfold in the real-world dynamic and complex environment.

Prior to joining Atos, he performed first Sales Engineering and later Application engineering roles for Avigilon, one of the world's largest security manufacturers.   Throughout his career, he has held key management positions within the industry and sought many certifications to further his career in security technology.