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AI workloads are bringing to light a longstanding constraint in data centers: as bandwidth rises, copper paths consume m...
30/10/2026

AI workloads are bringing to light a longstanding constraint in data centers: as bandwidth rises, copper paths consume more power and generate more heat.

Co-packaged optics (CPO) can help address this constraint. By bringing optical engines closer to compute and memory silicon, CPO shrinks the distance signals travel through copper.

In this Q&A, Sanjay Gangadhara, senior architect at Synopsys, discusses why the architectural approach is gaining urgency — and what it will take to make it work at scale.

Learn how co-packaged optics can reduce power loss in AI data centers and how reliability, cost, standards, and system design shape adoption.

AI factories are being rebuilt around the rack.The rack brings together the GPUs, memory, and storage needed to support ...
27/10/2026

AI factories are being rebuilt around the rack.

The rack brings together the GPUs, memory, and storage needed to support AI workloads — whether for inference, training, or other tasks. And it determines how efficiently those workloads deliver output.

As the industry pushes for increasingly powerful racks, it’s fair to ask why these advances are needed and what they enable. Next-generation AI factories will be able to run much bigger models, deliver real-time multimodal inference, and support agentic workflows at a practical cost per token.

In other words: more performance, more efficiency, more possibility.

Explore how 3D stacking, custom interface IP, co-packaged optics, and rack-scale co-design reduce cost per token for bigger models and agentic AI.

When asked what concerns him most about the state of the semiconductor industry, Synopsys President and CEO Sassine Ghaz...
22/10/2026

When asked what concerns him most about the state of the semiconductor industry, Synopsys President and CEO Sassine Ghazi didn’t hesitate.

“The pace, the pace, the pace,” he said.

The answer came during a fireside chat at SEMICON West with Ed Sperling, editor-in-chief of Semiconductor Engineering. The two covered autonomous agents, 3DICs, digital twins, and physical AI, and each topic came back to the same pressure point: AI is changing both the chips the industry builds and the way engineers build them, faster than traditional development cycles can handle.

“I’ve never seen a pace and system-level co-design and complexity like what we’re dealing with right now,” Ghazi said.

Synopsys CEO Sassine Ghazi explores how autonomous AI, digital twins, and system-level co-design are transforming the future of engineering.

AI factories bring large numbers of CPUs, GPUs, and other AI accelerators together to train models, run inference, and e...
20/10/2026

AI factories bring large numbers of CPUs, GPUs, and other AI accelerators together to train models, run inference, and execute other AI workloads at scale. Their combined processing capacity is enormous, but the system can only deliver that performance if data moves fast enough to keep pace.

These processors must exchange data with memory, with each other, and with components throughout the rack. If the interfaces cannot move that data at the required speed and efficiency, processors spend time waiting instead of working.

Discover how bandwidth, latency, energy per bit, interface IP, and optical connectivity determine usable AI factory performance.

As the technology industry transitions from general-purpose compute to AI workload optimization, the design focus is shi...
13/10/2026

As the technology industry transitions from general-purpose compute to AI workload optimization, the design focus is shifting from the server to the rack.

The rack — not the servers within it — is what ultimately determines computing efficiency, now measured in tokens per watt or tokens per dollar. Improving that efficiency requires everything to be designed, integrated, and optimized at the rack level, from compute and memory to interconnects and power.

Bringing these elements closer together helps in obvious ways. Shorter interconnects and lower latency improve performance, while better power efficiency and space utilization lower total cost of ownership.

But those gains come with consequences too, and you cannot address them in isolation.

See why AI factory racks must be designed as one system, from liquid cooling and 800-volt power to multi-die silicon and simulation.

  𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬-ը 𝐒𝐢𝐥𝐢𝐜𝐨𝐧 𝐌𝐨𝐮𝐧𝐭𝐚𝐢𝐧𝐬 𝟐𝟎𝟐𝟔-ի բրոնզե գործընկերն է։🚀 Synopsys Armenia -ի առաքելությունն է զորացնել նորարարներին՝ ...
13/10/2026


𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬-ը 𝐒𝐢𝐥𝐢𝐜𝐨𝐧 𝐌𝐨𝐮𝐧𝐭𝐚𝐢𝐧𝐬 𝟐𝟎𝟐𝟔-ի բրոնզե գործընկերն է։

🚀 Synopsys Armenia -ի առաքելությունն է զորացնել նորարարներին՝ առաջ մղելու մարդկության առաջընթացը։

Աշխարհում խելացի տեխնոլոգիաներն ամենուր են՝ փոխակերպելով մարդկանց առօրյա կյանքը։ Այս համատարած խելացիությունը պայմանավորված է երեք հիմնական միտումներով՝ արհեստական բանականությամբ, ծրագրային ապահովմամբ սահմանվող համակարգերով և կիսահաղորդչային տեխնոլոգիաների լայն տարածմամբ։ Տասնամյակներ շարունակ 𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬-ը եղել է համատարած խելացիությունը հնարավոր դարձնող տեխնոլոգիաների շարժիչ ուժը:

💡𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬-ը սիլիկոնից մինչև համակարգեր ինժեներական լուծումների առաջատարն է՝ հնարավորություն տալով հաճախորդներին արագ ստեղծել AI-ով աշխատող նորարարական արտադրանքներ։ Ընկերությունը տրամադրում է առաջատար լուծումներ կիսահաղորդչային նախագծման, մտավոր սեփականության (IP), մոդելավորման և վերլուծության համար։ 𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬-ը սերտորեն համագործակցում է տարբեր ոլորտներում գործող մեր հաճախորդների հետ՝ առավելագույնի հասցնելու նրանց հետազոտությունների և մշակումների (R&D) ներուժն ու արդյունավետությունը՝ այսօր խթանելով այն նորարարությունը, որը ոգեշնչում է վաղվա ստեղծարարությունը։ Ընկերությունները վստահում են 𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬-ին նոր տեխնոլոգիաների զարգացումն առաջնորդելու հարցում՝ նրանց օգնելու առանց փոխզիջումների ավելի արագ դուրս գալ շուկա։

✨ 𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬-ի տեխնոլոգիաները՝ Ձեր նորարարությունը

~9 միլիարդ ԱՄՆ դոլար եկամուտ (վերջին 12 ամիսների տվյալներով՝ TTM)
~25,345 աշխատակից
>25% ներդրում R&D-ում

🏆 #1-ը Էլեկտրոնային նախագծման ավտոմատացման ոլորտում
Առաջատարը առաջատար տեխնոլոգիական նորմերով նախագծման ոլորտում

🏆 #1-ը ինտերֆեյսային և հիմնարար մտավոր սեփականության ((IP) ոլորտում
Առաջատարը տարբեր գործարանների համար առաջադեմ տեխնոլոգիական նորմերով IP լուծումների ոլորտում

🏆 #1-ը մոդելավորման և վերլուծության ոլորտում
Առաջատարը բազմաֆիզիկական մոդելավորման ոլորտում

👉 Միացի՛ր 𝐒𝐢𝐥𝐢𝐜𝐨𝐧 𝐌𝐨𝐮𝐧𝐭𝐚𝐢𝐧𝐬 𝐒𝐮𝐦𝐦𝐢𝐭 & 𝐄𝐱𝐩𝐨 𝟐𝟎𝟐𝟔-ին
🔗 https://silicon-mountains.org/
📅 2026 թ․ նոյեմբերի 3–4
📍 Dvin Music Hall

🏛 𝐒𝐢𝐥𝐢𝐜𝐨𝐧 𝐌𝐨𝐮𝐧𝐭𝐚𝐢𝐧𝐬 𝐒𝐮𝐦𝐦𝐢𝐭 & 𝐄𝐱𝐩𝐨 𝟐𝟎𝟐𝟔 միջոցառումը կազմակերպում է ՏՀՏ գործատուների միությունը՝ ՀՀ բարձր տեխնոլոգիական արդյունաբերության և արհեստական բանականության նախարարության աջակցությամբ։

______

𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬 is the Bronze Partner of 𝐒𝐢𝐥𝐢𝐜𝐨𝐧 𝐌𝐨𝐮𝐧𝐭𝐚𝐢𝐧𝐬 𝟐𝟎𝟐𝟔!

🚀 𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬'𝐬 Mission is to Empower Innovators to Drive Human Advancement

Intelligent technology is everywhere, shaping everyday life. This pervasive intelligence is driven by three major trends: artificial intelligence, software-defined systems and silicon proliferation. For decades we’ve been a driving force of the technologies that make pervasive intelligence possible.

💡𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬 is the leader in engineering solutions from silicon to systems, enabling customers to rapidly innovate AI-powered products. They deliver industry-leading silicon design, IP and simulation and analysis solutions. 𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬 partners closely with their customers across a wide range of industries to maximize their R&D capability and productivity, powering innovation today that ignites the ingenuity of tomorrow. Companies trust 𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬 to pioneer new technologies to help them get to market faster, without compromise.

✨𝐒𝐲𝐧𝐨𝐩𝐬𝐲𝐬'𝐬 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲, 𝐘𝐨𝐮𝐫 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧™

~$9 billion revenue (TTM)
~25,345 employees
>25% R&D investment

🏆 #1 in EDA
Leader in Advanced Node Designs
Learn More

🏆 #1 in Interface & Foundation IP
Leader in Multi-Foundry Advanced-Node IP
Learn More

🏆 #1 in Simulation & Analysis
Leader in Multiphysics

👉 Join 𝐒𝐢𝐥𝐢𝐜𝐨𝐧 𝐌𝐨𝐮𝐧𝐭𝐚𝐢𝐧𝐬 𝐒𝐮𝐦𝐦𝐢𝐭 & 𝐄𝐱𝐩𝐨 𝟐𝟎𝟐𝟔
🔗 https://silicon-mountains.org/
📅 November 3–4, 2026
📍 Dvin Music Hall

🏛 𝐒𝐢𝐥𝐢𝐜𝐨𝐧 𝐌𝐨𝐮𝐧𝐭𝐚𝐢𝐧𝐬 𝐒𝐮𝐦𝐦𝐢𝐭 & 𝐄𝐱𝐩𝐨 𝟐𝟎𝟐𝟔 is organized by the 𝐔𝐄𝐈𝐂𝐓 with the support of the Ministry of High-Tech Industry and Artificial Intelligence of the Republic of Armenia.

08/10/2026

Հայտնի է բարձր տեխնոլոգիաների ոլորտում համաշխարհային ներդրման համար ՀՀ պետական մրցանակի արժանացած 17-րդ դափնեկրի անունը

🎥 ATV Armenian Television / Ա ԹԻՎԻ հեռուստաընկերություն

A new wave of intelligent machines will soon be unleashed: robots and other physical AI systems that can perceive their ...
08/10/2026

A new wave of intelligent machines will soon be unleashed: robots and other physical AI systems that can perceive their environment, make decisions on the fly, and act autonomously.

“AI is already expanding beyond the data center and increasingly finding its way into edge form factors,” says Olivier Blanchard, research director and AI devices lead at The Futurum Group.

That expansion is expected to accelerate in the years ahead. According to The Futurum Group, the total addressable market for robotics will grow from $83 billion in 2025 to $391 billion by 2035.

“The acceleration in R&D funding that lays the groundwork for the next wave of AI-driven disruption — one that will usher the deployment, at scale, of complex mobile and humanoid robots — is already well underway,” Blanchard says.

Turning that investment into deployable physical AI systems requires a new form of engineering: holistic silicon-to-system design, validation, and physics-aware training. Because system behavior depends on the interaction of silicon, sensors, software, mechanics, and power, those elements must be co-designed and validated together to help ensure safe, secure, and reliable operation. Just as important, the AI systems controlling those machines need training data grounded in physical reality.

Learn how silicon-to-system design, simulation, digital twins, and physics-aware training move physical AI from the lab to real-world deployment.

The high-performance computing (HPC) industry has always designed around the server. Servers went into racks, and racks ...
06/10/2026

The high-performance computing (HPC) industry has always designed around the server. Servers went into racks, and racks went into data centers. Units and facilities.

Today, data centers are being rearchitected as AI factories, purpose-built to process AI workloads at scale. And that is shifting the design point — from the server to the rack.

This pulls the unit of compute up from the server and the level of optimization in from the data center. The rack sits between them, becoming the primary building block of the AI factory.

The reason is straightforward. In the rack you have GPUs, interconnects, memory, and storage — all connected to each other, fed by power and cooled to keep them running. When an AI task is submitted, many of these GPUs need to work together. Maybe 12 of them in one rack, as one system. And they all need to deliver optimized performance at the lowest cost per token.

That single requirement changes how the whole system is designed. When the rack becomes the place where work actually gets done, it stops being a container for servers and becomes the unit the industry architects from the ground up.

Learn why the rack is replacing the server as the unit of compute in AI factories, and how that shifts design toward cost per token and usable AI.

Last week was momentous for Synopsys.The latest edition of   brings together key updates from Investor Day, including ou...
05/10/2026

Last week was momentous for Synopsys.
The latest edition of brings together key updates from Investor Day, including our long-term growth strategy, an IP agreement with Amazon, a partnership with OpenAI, and more.
📧 Give it a read, and be sure to subscribe for future editions:

Welcome to Synopsys Signal, your one-stop shop for the latest updates on everything Synopsys. Last week was a momentous week for Synopsys.

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