Sensor fusion has been discussed for years for a diverse array of applications. However, it acquires a highly specialized design premise when it comes to automotive applications like advanced driver assistance systems (ADAS) and autonomous vehicles (AVs). Perception and sensor fusion systems are among the highly complex areas in ADAS and AV designs from a computational standpoint as they crunch all the data and determine what a vehicle is seeing. More specifically, sensor fusion provides the ability to merge information from radars, lidar (light detection and ranging) and cameras to produce a single model of the space around a vehicle--a crucial capability for ADAS and AV designs. This model is created as a result of balancing the strengths of the various sensors to formulate a more accurate picture of vehicle surroundings.

Industrial IoT systems with the intelligence to sort goods on the production line based on their size and quality. Autonomous vehicles that passengers can summon for rides. Drones that survey crops to optimize water consumption and yield. Machine learning (ML) at the embedded edge is blossoming and new applications are certain to emerge as the underlying ML technologies become easier to implement. SiMa.ai is one of the companies at the forefront of ushering in an age of effortless ML for the embedded edge.

Machine learning is on track to consume all the energy being supplied, a model that is costly, inefficient, and unsustainable. To a large extent, this is because the field is new, exciting, and rapidly growing. It is being designed to break new ground in terms of accuracy or capability. Today, that means bigger models and larger training sets, which require exponential increases in processing capability and the consumption of vast amounts of power in data centers for both training and inference. In addition, smart devices are beginning to show up everywhere. But the collective power numbers are beginning to scare people.

MOUNTAIN VIEW, CA, USA, Mar 9, 2022 – Synopsys is pleased to announce the Simpleware Release T-2022.03. The latest release of Simpleware software includes many new features and improvements, including the new shoulder CT tool in the Simpleware AS Ortho module, contour measurements, improved 3D printing capabilities, and aortic valve analysis. Join us on March 30, 2022 to see the new features in action. Register to watch live or to receive the on-demand recording to view at your own convenience. Synopsys' Simpleware software provides an industry-leading, comprehensive 3D image processing platform for handling 3D scan data.

From the Beatles Let It Be to John Coltrane's A Love Supreme and Radiohead's OK Computer, record labels often remaster classic albums from the greats in many genres of music. These higher fidelity remasters are a welcome treat for aficionados and mainstream fans alike. But what if I told you, just like Led Zeppelin's or Van Halen's greatest hits, semiconductor chips could be "remastered" as well, and these remasters could help bail us out of the current chip shortage? For some folks that would totally rock, pun intended, but let's take a step back and look at the problem and potential solutions at hand first. The process of designing and verifying chips like the modern processors, controllers and sensors in cars, for example, can take years and require millions of dollars of R&D.

The goal of chip design always has been to optimize power, performance, and area (PPA), but results can vary greatly even with the best tools and highly experienced engineering teams. Optimizing PPA involves a growing number of tradeoffs that can vary by application, by availability of IP and other components, as well as the familiarity of engineers with different tools and methodologies. For example, higher performance may be achieved with a larger processor, but it also can be done using smaller, more specialized processing elements with tighter integration of hardware and software. So even in the same area and with the same power budget, there are different ways of achieving the same goal, and the optimum mix may vary depending upon a specific domain or vendor's needs. This is made even more complex by the rising demand for security.

In a blog for Arm, Matthew Griffin of the 311 Institute warns that cybersecurity is an increasingly pressing problem, with large criminal organizations raking in large sums of money and attacks able to impact a wide range of physical systems. Cadence's Paul McLellan checks out Google's video encoder chip and how it helps lower the CPU recycles required by the vast number of videos uploaded to YouTube every minute. Synopsys' Nikhil Amin and Harsha Vardhan explain the basics of UPF, its importance in the power landscape, how to expand low-power signoff with custom mechanisms, and how to approach things like hard RAM and hard macro where the connectivity of low-power control signals is unclear. In a blog for Siemens, EmLogic's Espen Tallaksen argues that many FPGAs and ASICs could be designed more efficiently and with fewer bug iterations at all levels if more attention is paid to creating a good design architecture from the outset. Coventor's Timothy Yang uses process modeling to identify an ALD thickness that minimizes the type of pattern offset and device non-uniformity that can be caused by self-aligned quadruple patterning.

Samsung is making cutting edge chips by using artificial intelligence. The South Korean company has partnered with Synopsys, a leading chip design software firm, to create the new AI-powered features in their latest line of computer processor designs. Synopsys has a new tool that can help companies design and create chips with AI. The DSO.ai software can optimise the chip designs, which will accelerate semiconductor development to unlock novel chip designs, according to industry watchers. With years of cutting-edge semiconductor designers available for training algorithms to emulate human intelligence, this could be Synopsis's next breakthrough technology!

For decades, constant innovation in the world of semiconductor chip design has made processors faster, more efficient, and easier to produce. Artificial intelligence (A.I.) is leading the next wave of innovation, trimming the chip design process from years to months by making it fully autonomous. Google, Nvidia, and others have showcased specialized chips designed by A.I., and electronic design automation (EDA) companies have already leveraged A.I. to speed up chip design. Software company Synopsys has a broader vision: Chips designed by A.I. from start to finish. A.I. has already shown its staying power in the world of semiconductors, and moving forward, it could be involved like never before.

Recently, it issued a statement saying the next generation of mobile phone chips will be designed using AI. As Wired reported, Samsung will use the AI function (DSO.ai) FYI, Exynos chips are used in Samsung's smartphones and tablets (mainly in the Korean and European markets). Synopsys is one of the world's largest suppliers of chip design software (EDA). The chairman of this company said that DSO.ai is the first commercial AI software for processor design.