Arteris Articles

SemiWiki: Intelligence in the Fog

Kurt Shuler, VP Marketing at Arteris IP, and Bernard Murphy (SemiWiki) discuss the hottest domains in tech today - AI and automotive in this new SemiWiki blog:

Intelligence in the Fog

June 12, 2019 - By Bernard Murphy

AI is creeping into places we might not expect, such as communication infrastructure. Bernard Murphy learns from Kurt Shuler how AI and AI-centric design methods are becoming more important in this surprising domain.

By now, you should know about AI in the cloud for natural language processing, image ID, recommendation, etc, etc (thanks to Google, Facebook, AWS, Baidu and several others) and AI on the edge for collision avoidance, lane-keeping, voice recognition and many other applications. But did you know about AI in the fog? First, a credit – my reference for all this information is Kurt Shuler, VP Marketing of Arteris IP. I really like working with these guys because they keep me plugged in to two of the hottest domains in tech today – AI and automotive. That and the fact that they’re really the only game in town for a commercial NoC solution, which means that pretty much everyone in AI, ADAS and a bunch of other fields (e.g. storage) is working with them.

For more information, please visit the Arteris IP AI package webpage: http://www.arteris.com/flexnoc-ai-package

Topics: SoC semiconductor automotive ADAS artificial intelligence semiwiki kurt shuler flexnoc ai package noc interconnect

Semiconductor Engineering: Interconnect Prominence In Fail-Operational Architectures

 Arteris IP's Kurt Shuler, Vice President of Marketing, authored this latest article in Semiconductor Engineering about moving toward "Fail Operational"

Topics: SoC automotive ADAS semiconductor engineering kurt shuler ISO PAS 21448 noc interconnect

Semiconductor Engineering: Big Shift In Multi-Core Design

 Arteris IP's Kurt Shuler, Vice President of Marketing, adds commentary in this article in Semiconductor Engineering.

Big Shift In Multi-Core Design

April 3rd, 2019 - By Ann Steffora Mutschler

System-wide concerns in AI and automotive are forcing hardware and software teams to work together, but gaps still remain.

Minding the gap
There are indications that mindset is beginning to change, particularly in markets such as automotive where systemic complexity extends well beyond a single chip or even a single vehicle.

“In the past, if you were a software engineer, the thinking was, ‘I have this chip available. Here’s what I can produce with my software,'” said Kurt Shuler, vice president of marketing at Arteris IP. “Nowadays, especially in the ADAS side of things that have an AI component or some kind of programmable object detection for the ADAS functionality, or an AI chip—whether it’s for the data center, edge, inference or training—the thinking has shifted more to system-design decisions. If this is designed with this given set of software algorithms, it is clear what needs to happen at a system level from the hardware and software point of view. At what level of detail should I optimize this hardware for the particular software I expect to run? This means the hardware and the software are now much more tightly integrated in those use cases than they probably have ever been unless it’s a very detailed embedded application. So now, in the early stages of design for these types of chips, whether it’s the autonomous driving chips or the AI chips, the software architect is in there, too.”

This is a definite sign of progress. “Before, they didn’t care,” Shuler said. “The layer/API between hardware and software is becoming less generic and more specific for those kinds of use cases, solving those kinds of problems. What that means, though, is there are software guys who went to Stanford and trained on Java script and have no idea what a register is. Then there are hardware guys who have no idea what a hypervisor or object-oriented programming is.”

For more information on AI, please click on the Arteris FlexNoC AI Package webpage: http://www.arteris.com/flexnoc-ai-package.

Topics: SoC software automotive ADAS autonomous driving semiconductor engineering AI hardware noc interconnect

Semiconductor Engineering: The Race To Multi-Domain SoCs

 Arteris IP's CEO looks at how automotive and AI are Altering chip design in this article in Semiconductor Engineering;

The Race To Multi-Domain SoCs

February 7th,  2019 - By Ed Sperling

K. Charles Janac, president and CEO of Arteris IP, sat
down with Semiconductor Engineering to discuss the impact of automotive and AI on chip design. What follows are excerpts of that conversation.

SE: What do you see as the biggest changes over the next 12 to 24 months?
Janac: There are segments of the semiconductor market that are shrinking, such as DTV and simple IoT. Others are going through an investment phase, including automotive, AI/machine learning and China. You really want to be focused on those segments. 

SE: So does IP that’s being developed today look radically different than it did five years ago?
Janac:
Yes, everything is getting amazingly complex. What people are building right now are multi-domain SoCs. The CPU, which used to do all the work, does relatively less work. There are accelerators for vision and data analysis outside of the CPU subsystem. There are machine learning sections, some general-purpose, some very specific, all on-chip. There is a memory subsystem with very high-bandwidth memory and low latency. There also is functional safety. You need tremendous performance because a car is a supercomputer on wheels. The car has to be very efficient, because you need to deliver that compute power without water cooling. Power management becomes very sophisticated. And then there are functional safety and security subsystems to keep these safe from environmental and man-made issues.

SE: Where does the network on chip (NoC) fit into all of this?
Janac: All data goes through the NoC of the chip. There are opportunities for generating value from that. But the increase in complexity is increasing the number and sophistication of the interconnect parts of the chip. Before, you may have had networks on chip. Now you may have 20 or 30.

Topics: semiconductor automotive ADAS neural networks AI LIDAR flexnoc ai package noc interconnect ML AI SoC Designers chiplets