tag: Baya Systems

Foundry Capacity Is Limiting Who Competes At Leading Edge Nodes
By Ann Mutschler - 30 Apr, 2026 - Comments: 0

Key Takeaways: Leading-edge node access is increasingly reserved for hyperscalers, squeezing smaller chip developers. Chiplets and advanced packaging offer a path forward, but raise cost, complexity, and risk — especially for smaller teams. Chip architecture is now driven as much by capacity, yield, and economics as by technical goals. The benefits of device scaling are sl... » read more

From Standards To Systems: The Chiplet Era On Arm
By Nandan Nayampally - 30 Apr, 2026 - Comments: 0

For three decades, Arm didn’t just participate in industry transformation — it redefined it. From mobile to cloud to automotive, Arm’s architecture and the AMBA ecosystem have become the backbone of scalable compute. Now the industry faces its next structural shift: The era of monolithic SoCs is tapering and giving way to the era of chiplet systems. While complex SoCs are going to b... » read more

NoC Coherency Challenges Balloon With AI SoCs And Chiplets
By Liz Allan - 30 Apr, 2026 - Comments: 0

Key Takeaways Data movement, congestion, and energy efficiency are key determiners of whether compute is usable. Different processors bring various coherency challenges. For example, a cache-coherent NoC for CPUs is expensive and harder to verify than an I/O-coherent NoC for an accelerator. Designers need to balance top-down performance with bottom-up physical engineering to effect... » read more

AI Workloads Are Turning The Data Center Network Into A Combined Memory And Storage Fabric
By Saurabh Gayen - 26 Mar, 2026 - Comments: 0

Recent industry trends, including the release of NVIDIA’s Rubin platform (developer.nvidia.com), point to a growing consensus that AI inference is reshaping data center architecture in a fundamental way. As inference workloads become dominant, the data center network is no longer just a communication layer between servers. It is increasingly part of a distributed memory and storage hierarchy,... » read more

Memory Wall Gets Higher
By Brian Bailey - 26 Mar, 2026 - Comments: 1

Key Takeaways An increasing percentage of the chip area is consumed by the same amount of SRAM for each node shrink. The problem is not limited to leading-edge AI, as it will eventually impact even small MCUs and MPUs. Architectural changes may be required. Stacking SRAM chiplets on logic is possible but expensive. SRAM is a vital piece of all computing systems, but its fail... » read more

Data Boom Puts Pressure On NoCs, Fabrics
By Liz Allan - 26 Mar, 2026 - Comments: 0

Key Takeaways: NoC challenges, such as wiring congestion, timing closure, and performance, must be considered in tandem with topology and placement. Topologies can be customized to meet an application’s specific data flow needs, with a system containing multiple topologies to suit different data or zones. What is challenging for one type of system, such as an SoC, switch, or AI chi... » read more

AI Won’t Kill Verification IP, But It Will Redefine It
By Ann Mutschler - 26 Mar, 2026 - Comments: 0

Key Takeaways AI will enhance, not replace, verification IP by automating test generation and debug. Verification IP’s core value will increasingly lie in trust, accountability, and system-level realism, especially as designs become more complex, multi-die, and security-sensitive. AI shifts verification bottlenecks from execution to specification quality, raising expectations for c... » read more

Chip Industry Week In Review
By The SE Staff - 27 Feb, 2026 - Comments: 0

Big Deals and Fundings Rapidus secured US$1.7B in a new funding round from the Japanese government and the private sector to ramp 2nm production by next year. Open AI announced a $110B in new funding, with $30B from Nvidia, $30B from Softbank and $50B from Amazon. In a $100B multi-year deal, Meta will power its AI infrastructure with up to 6GW of AMD's GPUs. SambaNova and Intel ar... » read more

AI Starting To Simplify Design Of Programmable Logic
By Liz Allan - 26 Feb, 2026 - Comments: 0

Key Takeaways AI/ML and agentic tools are getting better at helping design and compile FPGAs, but downstream programming is slower to benefit. FPGAs historically have been designed using Verilog or VHDL, but higher-level languages could push more intelligence into compilers. ML tools can also help with mixed-signal co-design by automatically tuning DSP algorithms based on analog simu... » read more

Can A Computer Science Student Be Taught To Design Hardware?
By Liz Allan - 17 Feb, 2026 - Comments: 2

Key Takeaways New approaches are being devised and tested to address the talent shortage. Leveraging AI in design tools will help engineers become more efficient, and potentially could reduce the time it takes to train engineering students. EDA companies are looking at whether it's possible to train computer science and software engineers to become hardware engineers. A vari... » read more

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