AI Spectrum

Chip design is one of the most complex and challenging tasks
in the world, requiring specialized tools and knowledge far beyond what is
needed in other fields. High-level synthesis (HLS) is a key tool to help
address complexity and achieve efficient, optimized designs which are key in
both modern smart products and cutting-edge AI. HLS and AI have strong synergies
in improving ease of use, speed and efficiency.

In this episode, host Spencer Acain is joined by Russell
Klein, program director at Siemens EDA and a member of the Catapult HLS team to
examine what HLS is and how it intersects with AI. Russ explains how AI
benefits from HLS-designed chips and, in turn, how AI can help make a complex
tool like Catapult more powerful.

In this episode you will learn:

·        
What is high-level synthesis? (0:30)

·        
Improving efficiency with AI and HLS (2:38)

·        
The potential of AI-assisted chip design (5:35)

New technology can offer a lot of promise for improving on
long-standing techniques or developing innovative approaches to different
challenges but, when it comes to adopting these new technologies into complex
industries, there are always challenges to overcome. With advancements as
complex as artificial intelligence and agentic AI, unlocking their full
potential across the design and manufacturing process will take time, yet in so
doing, achieve new heights of what is possible.

In this episode, host Spencer Acain is joined Shirish More,
AI Program Product Manager at Siemens and Michael Taesch, Senior Director of
Product Management for NX Manufacturing to look at what it takes to deploy AI
in industry, the challenges both technical and personal that must be overcome
and why making the effort will be important in the years to come.

In this episode you will learn:

·      
What it takes to bring generative AI to industry
(0:51)

·      
The impact of AI industry going forward (7:31)

As it continues to develop, artificial intelligence is
starting to be applied across many different sectors of industry with equally
many different new technologies used to support that. Agentic AI represents one
of the latest ways AI can be applied to more complex tasks including complex
design and manufacturing processes that existing AI isn’t well suited for.

In this episode, host Spencer Acain is joined Shirish More,
AI Program Product Manager at Siemens and Michael Taesch, Senior Director of
Product Management for NX Manufacturing explore the future of agentic and
generative AI in design and manufacturing applications, what that will mean for
the design process, and the role of AI as an assistant going forward.

In this episode you will learn:

·      
The future role of AI in design and
manufacturing (0:58)

·      
What is agentic AI? (7:51)

·      
The transition from AI to coworker (12:23)

Bringing AI into industry isn’t something that can happen
all at once, rather, something that will happen gradually by applying AI to
individual areas where it can have the most impact. With that said, as these
foundations continue to grow, more complex, overarching AI applications will
begin to find their way into industry as well, offering greater flexibility and
possibility then traditional systems can.

In this episode, host Spencer Acain is joined by Dr. James
Loach, head of research at Senseye Predictive Maintenance to look at the
applications of LLMs and other, similar AI technologies to the vast store of
information available across a digital enterprise, and what that means for the
future of design and manufacturing.

In this episode you will learn:

·       What
comes after the time series foundation model? (0:35)

·       Expanding
AI to enterprise data (3:26)

·       The
broader role of AI in industry (9:50)

Interpreting different types of information is a task humans
are inherently good at with just a little guidance and, from there, conclusions
can be drawn and connections made. Artificial intelligence by contrast requires
much more training but is capable of analyzing information and building
connections in wholly different ways than humans, allowing for a novel
perspective on key data.

In this episode, host Spencer Acain is joined by Dr. James
Loach, head of research at Senseye Predictive Maintenance to explore the ways
AI can analyze and interpret industrial data, the similarities between text
generating LLMs and Senseye’s own time series foundation models. James also
delves into what it took to make the time series foundation model a reality.

In this episode you will learn:

·       The
similarities between text and time series data (00:36)

·       Challenges
of building a time series foundation model (05:50)