Hardware and Systems Engineering | Kisaco Research

Hardware and Systems Engineering

Hardware and Systems Engineering

Color: 
#1d3459
On Device ML
Vision
Edge Trade Offs
Software Engineering
Hardware and Systems Engineering

Author:

Todd Vierra

Director, Customer Engagement
BrainChip

Todd brings more than 25 years of engineering and technical sales expertise in chip design, electronic design automation, and intellectual property.  He joined BrainChip from ARM, where he was director of field sales engineers for more than 15 years, providing support for ARM processors in the Machine Learning, Internet of Things (IoT), embedded and automotive, client/mobile, and enterprise business divisions. He spent nearly seven years in high-speed ASIC design at Applied Micro Systems, and 4 years at Cadence Design Systems. At Nurlogic Design Inc., and Artisan Components Todd led the technical sales teams for digital and high-speed Analog IP. He has a BS Electrical, Electronics, and Communications Engineering and an MBA from Coleman University.

 

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Todd Vierra

Director, Customer Engagement
BrainChip

Todd brings more than 25 years of engineering and technical sales expertise in chip design, electronic design automation, and intellectual property.  He joined BrainChip from ARM, where he was director of field sales engineers for more than 15 years, providing support for ARM processors in the Machine Learning, Internet of Things (IoT), embedded and automotive, client/mobile, and enterprise business divisions. He spent nearly seven years in high-speed ASIC design at Applied Micro Systems, and 4 years at Cadence Design Systems. At Nurlogic Design Inc., and Artisan Components Todd led the technical sales teams for digital and high-speed Analog IP. He has a BS Electrical, Electronics, and Communications Engineering and an MBA from Coleman University.

 

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.

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
  • 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. 

... read more

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. 

Vision
NLP and Speech
Connectivity and 5G
Chip and Systems Design
On Device ML
Innovation at the Edge
Edge Trade Offs
Data Science
Hardware and Systems Engineering
Software Engineering
Strategy
Industry & Investment
Vision
NLP and Speech
Connectivity and 5G
Chip and Systems Design
Innovation at the Edge
Edge Trade Offs
On Device ML
Data Science
Hardware and Systems Engineering
Software Engineering
Strategy
Industry & Investment
Vision
NLP and Speech
Connectivity and 5G
Chip and Systems Design
On Device ML
Innovation at the Edge
Edge Trade Offs
Data Science
Hardware and Systems Engineering
Software Engineering
Strategy
Industry & Investment
Vision
NLP and Speech
Connectivity and 5G
Chip and Systems Design
On Device ML
Edge Trade Offs
Innovation at the Edge
Data Science
Hardware and Systems Engineering
Software Engineering
Strategy
Industry & Investment
Vision
NLP and Speech
Connectivity and 5G
Chip and Systems Design
On Device ML
Edge Trade Offs
Innovation at the Edge
Data Science
Hardware and Systems Engineering
Software Engineering
Strategy
Industry & Investment

The Meet & Greet is the perfect opportunity to reconnect with peers, expand your network, and discuss the state of ML across the cloud-edge continuum! Join attendees from both Edge AI Summit and AI Hardware Summit for this extended networking session.

We will soon announce a luminary guest speaker who will present in the middle of the function, followed by a drinks reception for both events.

Order of ceremony:
4:00 - 5:00 PM: Informal Networking
5:00 - 6:00 PM: Guest Keynote Speaker
6:00 - 7:00 PM: Drinks Reception

Vision
NLP and Speech
Connectivity and 5G
Chip and Systems Design
On Device ML
Edge Trade Offs
Innovation at the Edge
Data Science
Hardware and Systems Engineering
Software Engineering
Strategy
Industry & Investment