Startup Funding: Q1 2026


The new year started off with a bang for private semiconductor companies, with 18 garnering mega funding rounds exceeding $100 million, and two, Rapidus and Cerebras, reaching the $1 billion mark. Predictably, the vast majority of those are either designing chips primarily for AI inference workloads or attempting to overcome bandwidth limitations by improving interconnects from the chip level t... » read more

AI’s Potential And Limitations In Chip Design


Experts at the Table: Semiconductor Engineering sat down to discuss the opportunities and challenges of using AI in chip design, with Thomas Andersen, vice president for AI & Machine Learning at Synopsys; Sridhar Boinapally, senior director of analog/mixed signal tools/flow at Intel; Alex Starr, corporate fellow at AMD; Stuart Oberman, vice president for GPU hardware engineering at Nvidia; ... » read more

Memory Wall Gets Higher


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


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


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

AI Design Reshapes Data Management


Key takeaways: Integrating AI into chip workflows is pushing companies to overhaul their data management strategies, shifting from passive storage to active, structured, and machine-readable systems. As training and inference workloads grow, data movement, congestion, and energy efficiency become the dominant challenges, often surpassing raw compute capability. Proprietary and comple... » read more

Using Data And AI More Effectively In EDA


Key Takeaways The data being produced by EDA tools tends to be for human consumption and has weak semantics. Agents are attempting to create actionable information from unstructured data. The Model Context Protocol may provide AI with access to better data. Semiconductor design generates a lot of data, but how much of that is useful or currently being used by AI tools? And h... » read more

AI Starting To Simplify Design Of Programmable Logic


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

Verifying Scale-Up And Scale-Out In Data Centers


Semiconductor Engineering sat down to discuss challenges and solutions for data center build-out and build-up with Gordon Allan, Siemens EDA director of verification IP; Rishi Chugh, vice president of product marketing for network switching at Marvell; Saravanan Kalinagasamy, senior director of ASIC design and validation at Astera Labs; and Jalaj Gupta, product engineering lead at Siemens EDA. ... » read more

Can A Computer Science Student Be Taught To Design Hardware?


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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