Збирач потоків

submitted by /u/AutoModerator
[link] [comments]

Відкриття меморіальної дошки Марії Бігун (Леут) KPI4U-1 пт, 05/29/2026 - 23:02

Captured using Fujifilm XH2 and Laowa 65mm F2.8. Quite the beauty. submitted by /u/Nightrach [link] [comments]

Infineon Technologies AG of Munich, Germany, which provides power systems and IoT, has joined NVIDIA’s MGX AI Factory ecosystem to help transform power delivery for next-generation AI data centers. Infineon’s power management solutions will support NVIDIA’s MGX architecture and 800VDC power architecture, an open, modular reference architecture designed for AI factories in the agentic AI era. 800VDC MGX-compatible power racks help existing AI infrastructure to scale AI compute performance and power density, creating an upgrade path for future AI infrastructure...

Infineon Technologies AG of Munich, Germany is introducing a new 1300V silicon carbide (SiC) module within the HybridPACK Drive family that is capable of continuous operation at temperatures up to 205°C. Existing designs typically allow up to 175°C. This increase enables automotive OEMs and tier-1 suppliers to deliver higher peak and continuous output power from existing inverter designs or, in new designs, reduce system complexity and overall cost...

For full-year 2025, epiwafer and substrate maker IQE plc of Cardiff, Wales, UK has reported revenue of £97.3m, down 17.6% on 2024’s £118m...

submitted by /u/DenkJu [link] [comments]

submitted by /u/1Davide [link] [comments]

Asymmetric network technologies such as ASA are becoming more important for transporting the increasing volume of sensor data in vehicles. At the same time, interoperability is harder to secure in a growing multi-vendor ecosystem. Rohde & Schwarz addresses this with a new ASA Motion Link compliance solution for the R&S RTP oscilloscope that focuses on standards-compliant testing, enables efficient device debugging, and ensures compatibility and seamless automotive communication.

The Automotive SerDes Alliance (ASA) was formed in 2019 with the ambition of providing an open standard for a multi-vendor ecosystem for high-speed asymmetric SerDes (Serializer/Deserializer) technology. It is perfectly optimal to carry data from high-resolution cameras, LiDAR, or radar sensors to the ECU or provide continuous data streams from the ECU to ultra-wide, high-definition displays, all the while ensuring that high-speed data transfers do not interfere with other sensitive vehicle electronics. With bandwidth capabilities scaling up to 16 Gbps per lane, it provides the scalable “nervous system” required for level 3 autonomous driving and beyond.

Rohde & Schwarz has been a long-standing member of the Alliance, contributing to the various technical committees to help further the open standard. The new R&S SPLUS-K105 option available on the R&S ScopeSuite+ software offers comprehensive electrical compliance testing for the latest ASA Motion Link standard. The software fully controls and automates the R&S RTP high-performance oscilloscope, allowing test engineers to seek guidance effortlessly through each test case via the step-by-step wizard and the web-based GUI. All relevant test cases for each speed grade (SG1 to SG5) are getting support from the software. In addition, users can also control the R&SZNB3000 and other supported VNAs for performing accurate return loss measurements.

The combination of oscilloscope, VNA, and compliance software provides capabilities for signal integrity analysis. The time domain impedance analysis in repeatable measurements is essential for verifying PHYs, ECUs, cables, and connectors, fostering reliable Automotive communication.

Further information can be found at https://www.rohde-schwarz.com/solutions/automotive-testing/in-vehicle-networks-and-ecu-testing/in-vehicle-networks-and-ecu-testing_231834.html.

The post Rohde & Schwarz strengthens Its In-Vehicle Networks Test Portfolio with the Launch of a New ASA-ML Compliance Solution appeared first on ELE Times.

From a Jena-based spin-off to a globally sought-after test and inspection specialist.

In May 2026, GÖPEL electronics celebrates its 35th anniversary. Founded in 1991 as a spin-off of Carl Zeiss Jena Measurement and Testing Technology, the company continuously evolves and is now one of the world’s leading providers of innovative test and inspection systems, with approximately 240 employees as well as locations and partners in more than 25 countries.

GÖPEL electronic was founded as a family business by Holger Göpel, Thomas Wenzel, and Manfred Schneider. To this day, this family-oriented character shapes the corporate culture. With Alice Göpel, Jörg Schneider, and Ricardo Wenzel, the next generation is now also actively involving in management and leadership roles. With annual revenue exceeding 40 million euros, GÖPEL electronic is now one of Thuringia’s leading medium-size technology companies.

A central component of the corporate strategy is consistent investment in research and development. Approximately one-quarter of annual revenue is invested in new technologies, products, and innovations. In doing so, the company lays the foundation for sustainable growth and long-term competitiveness each year. In addition to innovative inspection systems for electronics manufacturing, GÖPEL electronics’s core competencies include electrical test systems based on embedded JTAG and boundary scan, as well as test solutions specifically for the automotive sector.

As early as 2002, the company’s individual test platforms became the de facto standard in the European automotive industry. Worldwide, the AOI systems of the Basic Line, Vario Line, and Multi Line series ensure the highest quality in electronics manufacturing and production lines. In the field of boundary scan, GÖPEL electronic is also internationally known as a leading provider of complex solutions and their seamless integration into existing test environments.

At the same time, GÖPEL electronics is increasingly expanding into new industries. Test solutions for the medical, avionics, security technology, and industrial manufacturing sectors complement the portfolio for the automotive and electronics industries. In the field of inspection systems, the manufacturer is setting new standards this year with its new Multi Line AXI X-ray system, which is based on the new ASPECT technology, and opens up expanded possibilities for the inspection of electronic assemblies. In the Embedded JTAG sector as well, the focus is on innovative software solutions and new models that enable an unprecedented level of test depth in boundary scan. The systems are complemented by modern AI-supported functions that further simplify operation and analysis processes.

“35 years of GÖPEL electronics stand for innovative strength, reliability, and sustainable growth,” explains Alice Göpel, Managing Director of GÖPEL electronics. “We are proud to continue writing the success story of our family business together with our employees, customers, and partners. Many of our current solutions have emerged directly from our customers’ requirements. Together, we develop technologies that solve today’s challenges and help shape the future of electronics manufacturing.”

About GÖPEL electronic

GÖPEL electronic develops and manufactures innovative electrical and optical test, measurement, and inspection equipment for electronic components and printed circuit board assemblies, as well as industrial and automotive electronics systems. GÖPEL electronic has four business units:

•  Automotive Test Solutions
• Embedded JTAG Solutions
• Inspection Solutions AOI-AXI-SPI-IVS
• Industrial Function Test

The company is active worldwide, with its own subsidiaries as well as through distributors, and generated sales of approximately 40 million euros in 2023 with 240 employees. For further information, visit www.goepel.com/en.

The post 35 Years of Innovation from Jena: GÖPEL electronic Celebrates Its Anniversary appeared first on ELE Times.

Companies to collaborate on R&D to accelerate the introduction of the latest packaging technologies for next-generation AI chips and systems. Partnership will leverage Applied’s global network of innovation centers, including the new EPIC Center in Silicon Valley.

Applied Materials, Inc., the leader in materials engineering for the semiconductor industry, today announces that Broadcom Inc. will join Applied’s EPIC platform as an innovation partner to accelerate the development of next-gen chip packaging technologies critical to next-generation AI systems.

The explosive growth of AI has driven a surge in demand for high-performance, energy-efficient compute infrastructure. To address this demand, chipmakers and system designers are increasingly adopting the latest packaging techniques and heterogeneous integration of multiple chips, aiming to boost energy-efficient performance across their systems. To unlock AI’s full potential, the industry is developing a new set of packaging building blocks to dramatically increase the interconnect density and bandwidth of tomorrow’s systems.

“The EPIC platform is designed to drive co-innovation across the ecosystem to change the way semiconductor technologies are developed and commercialized,” said Gary Dickerson, President and CEO of Applied Materials. “This new model gives leading system designers like Broadcom early access to foundational innovations in materials and process equipment, providing an opportunity for deep collaboration to accelerate the introduction of new advanced packaging technologies.”

“Close collaboration with partners throughout the supply chain is critical to delivering the next generation of high-performance AI systems,” said Charlie Kawwas, President of the Semiconductor Solutions Group at Broadcom. “By bringing together Applied’s expertise in materials engineering with Broadcom’s leading capabilities in semiconductor and system design, we can accelerate the time to market for innovations in AI.”

Through the EPIC platform partnership, Broadcom will leverage the R&D work taking place across Applied’s global innovation centers – driving progress in advanced packaging capabilities for connecting multiple chips within a computing system.

“Innovation in advanced packaging is essential to enabling sustainable progress in the AI era,” said Dr. Prabu Raja, President of the Semiconductor Products Group at Applied Materials. “We look forward to working side-by-side with Broadcom engineers to explore new technologies for boosting performance-per-watt through advanced chip packaging. With our global innovation platform and the new EPIC Center in Silicon Valley, Applied is uniquely positioned to help chipmakers and system designers accelerate the journey from concept to commercialization.”

“Cross-ecosystem collaboration has never been more important to address the rising complexity of chips for AI systems,” said Dilip Vijay, Vice President and Head of Global Operations for Silicon Products at Broadcom. “System designers must navigate a complex array of solution paths and packaging architectures, while simultaneously driving a faster cadence of product introductions. Collaborating with Applied will provide earlier access to the foundational technologies needed to accelerate progress in advanced packaging.”

Applied’s new EPIC (Equipment and Process Innovation and Commercialization) Center in Silicon Valley is the cornerstone of the company’s global EPIC platform. Representing the largest-ever U.S. investment in semiconductor equipment R&D, the center is designed from the ground up to dramatically reduce the time it takes to commercialize breakthrough technologies from early-stage research to full-scale manufacturing. The facility is on track to become operational in 2026.

Forward-Looking Statements

This press release contains forward-looking statements, including those regarding Applied’s investment and growth strategies, the development of new materials and technologies, industry outlook and technology requirements, the plans and expectations for the EPIC platform and Center, and other statements that are not historical facts. These statements and their underlying assumptions are subject to risks and uncertainties and are not guarantees of future performance. Factors that could cause actual results to differ materially from those shown by such statements include, without limitation: the demand for semiconductors and customers’ technology requirements; the ability to develop new and innovative technologies; the ability to obtain and protect intellectual property rights in key technologies; the ability to achieve the objectives of the EPIC platform and Center; and other risks and uncertainties described in Applied’s filings with the Securities and Exchange Commission, including Applied’s most recent Forms 10-K, 10-Q and 8-K. All forward-looking statements are based on management’s current estimates, projections, and assumptions, and Applied assumes no obligation to update them.

About Applied Materials

Applied Materials, Inc. is the leader in materials engineering solutions that are at the foundation of virtually every new semiconductor and advanced display in the world. The technology we create is essential to advancing AI and accelerating the commercialization of next-generation chips. At Applied, we push the boundaries of science and engineering to deliver material innovation that changes the world. Learn more at www.appliedmaterials.com.

The post Applied Materials Announces Broadcom as EPIC Innovation Partner appeared first on ELE Times.

Got around to making an enclosure for this little guy. It's was done a lot more strategically than the first one I made. I went with colorful popsicle sticks this time around. I had the forethought to have the audio sockets stick out from the pcb just enough that when I made the enclosure, they'd go through a side and be flush with the surface so connecting a cable is simple. I tried to photograph as much of the process as I could remember haha Someone asked for a schematic in the previous post. It's seems simple enough, but I'll see if I get around to making one in a follow up if you're interested in making a passive mixer of your own. Be warned that making it like this in this particular form factor had it's frustrations if you're not experienced with tinkering. It took a lot of patience. I've got some additional cleaning up to do for the edges, but it turned out nice enough I think. submitted by /u/Edboy796 [link] [comments]

$2.59 Trillion in AI Spending Excels by Vendors and Hyperscalers, with Enterprises Yet to Flex Spending Potential.

Worldwide spending on AI is forecast to total $2.59 trillion in 2026, a 47% increase year-over-year, according to Gartner, Inc., a business and technology insights company.

“Through the next several years, the need for capacity will make AI infrastructure, including AI-optimized IaaS, AI-optimized servers, AI network fabric, AI processing semiconductors and devices, the largest segment of the market, accounting for over 45% of spending, which will be driven by vendors,” said John-David Lovelock, Distinguished VP Analyst at Gartner. “Within this segment, spending on AI-optimized servers will triple over the next five years to become the largest subsegment, as cloud services providers expand capacity in anticipation of the workloads created by GenAI models and agentic workflows.”

Enterprises are expanding their use of both GenAI models with built-in existing software applications and the latest AI agents within multiple workflows. Model consumption increases through multistep processes and integration into broad suites of tools as enterprises recognize the potential value of agentic automation. This dynamic means that the short-term outlook for AI models would increase to 110% growth in 2026, adding $6 billion in spending this year.

Worldwide AI Spending by Market, 2025-2027 (Millions of U.S. Dollars)

Market	2025	2026	2027
AI Services	436,351	585,527	759,418
AI Cybersecurity	25,920	51,347	85,997
AI Software	282,897	453,209	638,431
AI Models	15,494	32,604	59,161
AI Platforms for Data Science and Machine Learning	21,292	29,928	42,639
AI Application Development Platforms	6,587	8,416	10,922
AI Data	826	3,126	6,480
AI Infrastructure	975,581	1,431,509	1,890,310
Total AI Spending	1,764,947	2,595,667	3,493,358

Source: Gartner (May 2026)

“Up to this point, AI spending has primarily been driven by technology companies and hyperscalers,” said Lovelock. Enterprises have yet to really flex their spending potential. That is coming, and 2026 will be the inflection year. Currently, organizations show limited appetite for using AI to drive disruptive enterprise change. Instead, they favor tactical AI initiatives with incremental improvements in efficiency and productivity.

“For this reason, CIOs face challenges in proving the value from AI investments and demonstrate tangible business outcomes,” said Lovelock. “Aligning AI initiatives with strategic business objectives is the essential step for success. This incremental approach persists despite AI hype and valuations that reflect aspirations to transform the broader economy.”

Gartner is the World Authority on AI

Gartner is the indispensable partner to C-Level executives and technology providers as they implement AI strategies to achieve their critical priorities. The independence and objectivity of Gartner insights provide clients with the confidence to make smart decisions and unlock the full potential of AI. Clients across the C-Level are using Gartner’s proprietary AskGartner AI tool to determine how to leverage AI in the business. With more than 2,500 business and technology experts, 6,000 written insights, and more than 4,000 AI use cases and case studies, Gartner is the world’s authority on AI. More information can be found here.

Gartner IT Symposium/Xpo

The outlook on AIs during Gartner IT Symposium/Xpo is the world’s most important conference for CIOs and other IT executives. Gartner analysts and attendees explore how to become agents of change in their organizations and harness AI for successful digital transformation. Follow news and updates from the conferences on X and LinkedIn using #GartnerSYM, and on the Gartner Newsroom.

Upcoming dates and locations for Gartner IT Symposium/Xpo include:
September 14-16, 2026 | Gold Coast, Australia
October 19-22, 2026 | Orlando, FL
November 4-6, 2026 | Yokohama, Japan
November 9-12, 2026 | Barcelona, Spain
November 16-18, 2026 | Kochi, India

About Gartner for High Tech Leaders and Providers

Gartner for High Tech Leaders and Providers equips tech leaders and their teams with role-based best practices, industry insights, and strategic views into emerging trends and market changes to achieve their mission-critical priorities and build the successful organizations of tomorrow. Additional information is available at www.gartner.com/en/industries/high-tech.

 

 

The post Gartner Forecasts Worldwide AI Spending to Grow 47% in 2026 appeared first on ELE Times.

Науковці НН ІМЗ ім. Є.О. Патона конструюють новітні матеріали на атомному рівні Інформація КП пт, 05/29/2026 - 12:00

Artificial intelligence (AI) silicon is increasingly defined not only by compute capability, but by how data moves through the system. Modern AI SoCs, edge AI processors, automotive compute platforms, and AI accelerators depend on large volumes of data moving among compute engines, memory systems, sensor interfaces, accelerators, chiplet interfaces, firmware controllers, and I/O.

This is why network-on-chip (NoC) architectures have become essential. An NoC provides the internal communication fabric that helps organize routing, arbitration, bandwidth allocation, quality of service, congestion management, and latency behavior inside complex AI silicon.

But it’s important to make a clear distinction.

An NoC is part of the chip execution architecture. It’s not the same as the external signaling interfaces that bring data into or out of the chip.

External signals may arrive through MIPI, SerDes, PCIe, CXL, UCIe, LPDDR, HBM, Ethernet, CAN, or other physical and protocol interfaces. Those interfaces use PHYs, controllers, and protocol layers to move signals into a form the SoC can process internally. Once inside the chip, the NoC routes transactions among internal blocks such as CPUs, NPUs, GPUs, DSPs, memory controllers, sensor-processing blocks, safety islands, and I/O controllers.

In other words, external interfaces move signals into and out of the silicon. The NoC organizes internal data movement inside the silicon. This distinction matters because data movement is not the same as evidence governance.

NoC is not the governance layer

An NoC can move data efficiently, but it does not determine whether a later system symptom was caused by NoC behavior, timing weakness, placement and routing (P&R), power delivery, package behavior, firmware scheduling, workload bursts, or thermal conditions.

For example, a system may observe:

• Accelerator stalls
• Latency spikes
• Traffic congestion
• Power bursts
• Voltage droop
• Timing-margin loss
• Thermal hotspots
• Memory-access delays
• Chiplet-interface errors
• Workload-dependent failures

These symptoms may involve NoC activity, but NoC activity alone does not prove NoC causality.

A thermal hotspot may correlate with NoC traffic, but the root cause could also be local transistor density, P&R, clocking behavior, package thermal resistance, power-delivery weakness, firmware scheduling, workload concentration, sensor placement, board conditions, or cooling limitations.

A latency spike may appear in an NoC counter, but the underlying contributor could be memory-controller contention, cache behavior, firmware policy, workload burstiness, arbitration settings, clock-domain crossing, timing margin, or external I/O behavior.

This is the central point: NoC may be one possible contributor to observed AI silicon behavior, but it should not be assumed to be the source of the problem without admissible evidence.

Where SEGA-AI fits

SEGA-AI does not replace NoC architecture, RTL design, physical implementation, timing closure, P&R, verification, or post-silicon debug. Its role is different.

SEGA-AI defines how NoC-related observability, telemetry, counters, workload traces, firmware logs, power data, thermal data, package evidence, and system behavior are qualified before any root-cause conclusion or lifecycle-governance decision is made.

The contribution is not SEGA-AI sees a problem and knows the cause. The contribution is SEGA-AI governs the evidence path required before the system is allowed to assign cause, trigger corrective action, refine assumptions, or update lifecycle policy.

This distinction is essential for complex AI silicon because many physical, architectural, and operational mechanisms can produce similar symptoms.

• A detected hotspot is a symptom
• A detected latency spike is a symptom
• A voltage droop event is a symptom
• An accelerator stall is a symptom

SEGA-AI asks whether the evidence behind that symptom is mature enough, synchronized enough, causally valid enough, and admissible enough to support a decision.

From symptom to evidence through CEMH

Consider a realized AI SoC where telemetry reports a localized hotspot during a high-throughput workload. At level 1, with raw data, the system has only a thermal sensor observation: a localized temperature rise was detected. This observation is useful, but it’s not yet decision-ready evidence.

At level 2, with interoperable data, the temperature reading can move into a diagnostic environment, firmware log, validation database, or fleet-monitoring system. But movement does not create authority. The hotspot may be visible and accessible, but its cause is still unknown.

At level 3, with normalized evidence, the observation is linked to the context required for interpretation:

• Workload type
• Timestamp and runtime epoch
• Firmware policy state
• NoC traffic counters
• Accelerator utilization
• Memory-controller activity
• Voltage droop measurements
• Clock and power state
• Floorplan region
• Thermal sensor location
• Package thermal path
• Board and cooling condition
• Package lot and assembly history
• Validation correlation status

Only at this stage can the event begin to be compared across domains.

At level 4, with admissible evidence, the evidence must pass the Trusted Convergence Governance (TCG) gate. The system must confirm provenance, synchronization, realization-state validity, causal relevance, measurement confidence, and chain-of-custody integrity before the hotspot data can influence a convergence decision.

At level 5, with convergence-authoritative evidence, the system has enough qualified evidence to support bounded action or lifecycle refinement. That action may be a firmware policy adjustment, workload throttling, degraded mode, validation update, package constraint refinement, or future design-rule feedback.

• The hotspot may be related to NoC congestion.
• It may be related to accelerator placement.
• It may be related to P&R density.
• It may be related to package thermal resistance.
• It may be related to voltage droop and increased local switching.
• It may be related to firmware scheduling or workload concentration.
• The purpose of SEGA-AI is to prevent premature conclusions.
• A thermal sensor does not prove NoC causality.
• An NoC counter does not prove package causality.
• A voltage droop event does not prove timing causality.

SEGA-AI requires that the evidence mature through Convergence Evidence Maturity Hierarchy (CEMH) and pass TCG admissibility before any root-cause conclusion or lifecycle-governance action receives authority.

The role of CEMH, TCG, and GFL

Within the SEGA-AI framework, three layers are especially relevant.

Convergence Evidence Maturity Hierarchy (CEMH) defines how information matures from raw observation into convergence-authoritative evidence. A thermal sensor value, NoC counter, voltage monitor, or firmware trace begin as raw or interoperable data. It does not become decision-ready evidence until it has been contextualized, synchronized, qualified, and connected to the correct realization state.

Trusted Convergence Governance (TCG) acts as the trust gate. It asks whether evidence preserves provenance, synchronization validity, realization-state consistency, causal relevance, and bounded authority before it influences a decision.

Governance for Lifecycle (GFL) asks whether the realized system can remain converged throughout operational life. It’s concerned not only with whether the chip worked at initial signoff, but whether chip, package, board, firmware, workload, and field behavior remain aligned over time.

Together, these layers prevent a common failure mode: mistaking observable behavior for proven causality.

Diagnostic evidence plan

This also changes how AI silicon should be planned before implementation. Here, SEGA-AI can contribute by helping define the diagnostic evidence plan.

• Which NoC counters are needed?
• Which congestion metrics should be exposed?
• Which workload tags must be preserved?
• Which timestamps and synchronization epochs are required?
• Which voltage, thermal, clock, and power monitors are needed?
• Which firmware traces must be connected to physical state?
• Which package and board conditions must be tracked?
• Which evidence fields are required to distinguish NoC behavior from timing, P&R, PDN, thermal, firmware, or package causes?

This does not mean SEGA-AI designs the NoC. It means SEGA-AI asks what evidence must exist later so that realized-system behavior can be interpreted correctly. That is the bridge between design intent and lifecycle governance.

Why data movement alone isn’t enough

NoC architectures are essential because AI silicon needs scalable internal communication. But moving data correctly inside the chip does not automatically explain system behavior after realization. An NoC may deliver a packet correctly while the system still experiences thermal drift. Likewise, a controller may report a valid transaction while the package creates a local thermal bottleneck.

Next, a firmware trace may show a workload transition while the underlying voltage margin is collapsing. Or a sensor may report a hotspot while the causal chain remains ambiguous. This is why observability must become governed evidence before it can support lifecycle decisions.

The key question is not only: Did the data move? The real question is: Is the observed behavior mature enough as evidence to support diagnosis, intervention, or lifecycle refinement? This distinction becomes especially important in edge AI and ADAS systems.

In an ADAS platform, camera, radar, lidar, IMU, wheel-speed, steering, and vehicle-state data enter through physical interfaces and controllers. Inside the AI SoC, the NoC routes internal traffic among image processors, AI accelerators, CPUs, memory controllers, safety islands, and I/O blocks.

The AI accelerator may detect pedestrians, lanes, vehicles, or collision risk. But if a late response, thermal event, inference delay, or braking-decision uncertainty is observed, the system should not automatically blame the NoC, the AI model, the memory controller, or the package. It must first build an admissible evidence chain.

This matters because ADAS is not only a performance application; it’s a safety-critical realization environment.

A latency spike or inference delay may affect warning time, braking distance, steering support, or driver handoff. In that context, clean data movement is not enough. The system must know whether the evidence supporting the decision is synchronized, causally valid, realization-consistent, and authoritative enough for action.

For low-risk edge AI applications, a wrong output may create inconvenience or cost. For ADAS, a wrong output may affect human safety. That changes the required evidence maturity.

A safety-critical output should not receive full action authority simply because data moved correctly through the chip. It should be supported by level 5 convergence-authoritative evidence or by a pre-qualified safety envelope that has already been validated through admissible evidence.

In SEGA-AI terms, the chain is:

Input evidence → local inference → confidence and uncertainty → synchronization check → causality check → TCG admissibility gate → bounded output authority

This is why edge AI and ADAS show the difference between data movement and evidence governance. The NoC may help move sensor data, model data, and inference results; but SEGA-AI governs whether the observed behavior is trustworthy enough to support diagnosis, intervention, degraded mode, fleet learning, or safety-critical action.

From execution fabric to governance framework

The NoC is an execution fabric; SEGA-AI is a governance framework. The NoC helps the chip move data; SEGA-AI helps the system determine whether observed behavior can be trusted as evidence. And these are complementary roles.

As AI silicon becomes more complex, the industry will need both: data-movement architecture to move information efficiently inside the chip, and evidence-governance architecture to determine whether observed behavior can support root-cause analysis, corrective action, lifecycle refinement, or fleet learning.

This becomes increasingly important as systems move from design into package, board, validation, deployment, runtime adaptation, and field operation. And this discussion is not only theoretical. If realized AI systems require governed evidence, then implementation must account for evidence maturity from the beginning.

That means the design and validation plan must define not only what data moves, but what data must later be observable, timestamped, correlated, and qualified. For example, if post-silicon validation or field operation needs to distinguish NoC congestion from P&R density, package thermal resistance, memory-controller contention, or firmware scheduling, then the required evidence must be designed into the system earlier.

This includes counters, monitors, timestamping, workload tags, synchronization epochs, sensor placement, firmware traceability, package-state linkage, and validation correlation methods. In SEGA-AI terms, the theoretical model becomes practical only when it’s translated into implementation artifacts: evidence fields, admissibility checks, traceability rules, synchronization requirements, gate criteria, diagnostic workflows, and lifecycle feedback paths.

This is why the next step after governance theory is implementation specification. A system cannot govern evidence it never planned to observe.

Silicon governance complementing NoC

AI silicon performance depends heavily on data movement. NoC architectures are essential because they organize internal communication among compute, memory, accelerators, controllers, chiplet interfaces, and I/O. But NoC observability is not the same as causality.

A latency spike, hotspot, voltage droop, or accelerator stall may involve NoC behavior, but it may also be driven by timing, P&R, power delivery, package thermal paths, firmware policy, workload behavior, or system-level conditions.

However, the role of SEGA-AI is not to replace NoC design. The role of SEGA-AI is to govern the evidence required before symptoms become conclusions and before conclusions become decisions.

For AI silicon, the next challenge is therefore not only moving data efficiently. It’s qualifying observed behavior into admissible, causally grounded, convergence-authoritative evidence. In short, interoperability moves data; admissibility qualifies evidence; and governed convergence closes decisions.

Dr. Moh Kolbehdari is senior director of IC/packaging at Socionext US.

Related Content

• Speeding AI SoC development with NoC-enabled tiling
• How data movement defines performance for AI silicon
• Why verification matters in network-on-chip (NoC) design
• Taming the beast: Memory efficiency in an AI/crypto world
• Automotive silicon in the era of AI, functional safety, and cybersecurity

The post From AI silicon observability to governed evidence appeared first on EDN.

На війні загинув випускник нашого університету — Реваз Гегечкорі kpi чт, 05/28/2026 - 22:48

Purdue University has formed a strategic partnership with Taiwan-based GeChi Compound Semiconductor Co (GCCS) to accelerate the commercialization of silicon carbide (SiC). The collaboration targets the critical thermal, power and 6G bottlenecks currently constraining the next generation of high-compute infrastructure...

A fundamentally new approach is required to fuse AI-driven automation and multiphysics intelligence across the entire design and manufacturing flow. That was the crux of the keynote by Synopsys president and CEO Sassine Ghazi at the SAFE Forum 2026, held by Samsung Foundry in San Jose, California.

Ghazi especially mentioned design and technology co-optimization (DTCO) initiatives for synthesis and layout, as well as sign-off, delivering meaningful power, performance, and area (PPA) enhancements. He also talked about the design partnership between Samsung Foundry and Synopsys, which encompasses production-ready, AI-powered EDA tools, certified interface IP, and silicon-based test capabilities.

Hyung-Ock Kim, VP and head of the Foundry Design Technology Team at Samsung Electronics, echoed similar views, stressing the need for close alignment across design, test, and manufacturing to ensure the success of AI and multi-die designs on advanced nodes.

He also presented an update on Samsung Foundry’s collaboration with Samsung for production-ready, AI-powered digital and analog flows. “Our continued close collaboration with Synopsys delivers silicon-based, customer-validated solutions that help our customers reduce design integration risk, improve silicon predictability, and move confidently from design to production for their most innovative solutions,” Kim said.

Ravi Subramanian, chief product management officer at Synopsys, briefed on AI-powered digital and analog flows for Samsung’s second- and third-generation 2-nm processes. “As designs become more heterogeneous, customers need production-ready, silicon-proven solutions that address complexity and minimize risk from silicon to systems,” he said. “Our work with Samsung Foundry translates years of DTCO and silicon learning into enablement that helps our customers get their advanced designs to market quickly and with confidence.”

The partnership encompasses AI-powered EDA flows, multiphysics sign-off, interface IPs, and silicon-based test patters. Source: Synopsys

Below are the five key tenets of this design partnership between Samsung Foundry and Synopsys.

1. Production-ready digital and analog flows for 2-nm process

As part of DTCO initiatives, Synopsys Fusion Compiler delivers measurable power and performance improvements in the third-generation 2-nm class process compared to the second-generation 2-nm class process.

2. Sign-off with certified multiphysics capabilities

Synopsys PrimeShield process sensitivity analysis and PVT Explorer support design-specific optimization and engineering change order (ECO) decisions during sign-off. That leads to frequency improvement of up to 2.7% within 5% leakage current degradation. Moreover, Synopsys Totem-SC, a newly certified electromigration (EM) and IR drop analysis solution, improves silicon design power integrity and reliability in second-generation 2-nm and 4-nm class processes.

3. 3DIC with hybrid copper bonding

Samsung Foundry and Synopsys have joined hands to enable scalable 3D multi-die designs through certified multiphysics signoff solutions delivered within Synopsys 3DIC Compiler, a unified exploration-to-signoff platform being validated on a hybrid copper bonding (HCB) 3D test chip.

This platform brings together planning, implementation, and multiphysics analysis to enable co-optimization across integrated compute, memory, and advanced packaging systems for Samsung’s 3DIC solutions with HCB technology. And it replaces manual, margin-based approaches with automated, AI-driven system optimization to accelerate productivity and enhance the quality of results (QoR).

4. Interface and foundation IP portfolio

Synopsys offers a broad portfolio of IPs across Samsung Foundry’s advanced processes, ranging from 14-nm, 8-nm, and 5-nm processes to the latest 4-nm and second-generation 2-nm nodes. The interface IP offerings cover UCIe, PCIe 7.0, 112G/224G, MIPI, LPDDR6, DDR5 MRDIMM Gen2, and USB4. Likewise, its foundation IPs include embedded memories, logic libraries, GPIOs, security IP, and Silicon Lifecycle Management (SLM).

5. AI-powered tests

Samsung Foundry and Synopsys are also applying silicon-proven methodologies to design-for-test (DFT) and manufacturing test capabilities to reduce test cost and improve test quality for designs on advanced process nodes. Furthermore, physically aware tests and failure diagnosis at the die and multi-die level improve test quality and failure analysis turnaround time with results validated on silicon at Samsung Foundry.

For instance, Samsung Foundry teams employed Synopsys TestMAX along with AI-assisted automatic test pattern generation (ATPG) technologies to reduce test patterns and test cycles by up to 20%. Samsung Foundry customers leveraging these AI-powered, silicon-based design and manufacturing test capabilities acknowledge test efficiency improvements of up to 20%.

Related Content

• AI features in EDA tools: Facts and fiction
• EDA’s big three compare AI notes with TSMC
• What is the EDA problem worth solving with AI?
• How generative AI puts the magic back in hardware design
• 4 basic steps in implementing an AI-driven design workflow

The post 5 takeaways from Samsung Foundry’s design tie-up with Synopsys appeared first on EDN.

RF, microwave and power component distributor RFMW of San Jose, CA, USA (a Division of Exponential Technology Group Inc) has announced a global distribution agreement with South Korea-based RFHIC Corp...